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A Characterization Approach for the Review of CAD Systems Designed for Breast Tumor Classification Using B-Mode Ultrasound Images

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Abstract

For screening breast tumors, different imaging modalities like ultrasound, mammography, computed tomography (CT), magnetic resonance imaging (MRI) have been utilized. Mammography and CT use ionizing radiations and hence are not preferred for pregnant women. Even though MRI has high sensitivity for differentiating between breast tumor types, it is costlier and not available everywhere. Therefore, ultrasound is used more prominently for screening of breast tissue due to its ease of use, portability, low cost and safety. Ultrasound images are marred by speckle noise, hence an accurate diagnosis of abnormalities is challenging even for experienced radiologists. Therefore, increasing amount of interest has been observed among researchers to address these limitations and enhance the diagnostic potential of ultrasound images. Accordingly, in the present work, an exhaustive review of machine learning and deep learning based computer aided diagnostic (CAD) system designs has been conducted and brain storming diagrams have been used to indicate the characterization approaches for each stage i.e. (i) datasets, (ii) pre-processing methods, (iii) data augmentation methods, (iv) segmentation methods, (v) feature extraction methods, (vi) feature selection methods, (vii) classification methods and (viii) evaluation metrics. The paper also presents (a) clinically significant sonographic features for differentiating between breast tumor types, (b) achievements made in the design of CAD systems for breast tumor classification and (c) future challenges in designing such systems. The directions for future research to further enhance the diagnostic potential of ultrasound imaging modality for differential diagnosis between different breast abnormalities have also been highlighted.

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References

  1. Kriti, Virmani J, Dey N, Kumar V (2015) PCA-PNN and PCA-SVM based CAD systems for breast density classification. In: Hassanien AE et al (eds.), Applications of Intelligent Optimization in Biology and Medicine, Spriner, Cham, vol. 96, pp. 159–180

  2. What is cancer? MNT Knowledge Center, [online], http://www.medicalnewstoday. com/info/cancer-oncology/(Accessed: Dec 2014)

  3. Consensus document for management of breast cancer. Indian Council of Medical Research, [online], http://www.icmr.nic.in/guide/cancer/Breast_Cancer.pdf (Accessed: Apr 2018)

  4. Bassett LW, Ysrael M, Golf RH, Ysrael C (1991) Usefulness of mammography and sonography in women less than 35 years of age. Radiology 180(3):831–835

    Article  Google Scholar 

  5. Brem RM, Lenihan MK, Lieberman J, Torrente J (2015) Screening breast ultrasound: past, present and future. Am J Roentgenol 204(2):234–240

    Article  Google Scholar 

  6. Crystal P, Strano SD, Shcharynski S, Koretz NJ (2003) Using sonography to screen women with mammographically dense breasts. Am J Radiol 181(1):177–182

    Google Scholar 

  7. Gordon PB (2002) Ultrasound for breast cancer screening and staging. Radiol Clin North Am 40(3):431–441

    Article  Google Scholar 

  8. Tohno E, Ueno E, Watanabe H (2009) Ultrasound screening of breast cancer. Breast Cancer 16:18–22

    Article  Google Scholar 

  9. Velez N, Earnest DE, Staren ED (2000) Diagnostic and interventional ultrasound for breast disease. Am J Surg 180(4):284–287

    Article  Google Scholar 

  10. Acharya UR, Faust O, Molinari F, VinithaSree S, Junnarkar SP, Sudarshan V (2015) Ultrasound-based tissue characterization and classification of fatty liver disease: a screening and diagnostic paradigm. Knowl Based Syst 75:66–77

    Article  Google Scholar 

  11. Andrade A, Silva JS, Santos J, Belo-Soares P (2012) Classifier approaches for liver steatosis using ultrasound images. Procedia Technol 5:763–770

    Article  Google Scholar 

  12. Badawi AM, Derbala AS, Youssef ABM (1999) Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images. Int J Med Inform 55(2):135–147

    Article  Google Scholar 

  13. Kadah YM, Farag AA, Zurada JM, Badawi AM, Youssef ABM (1996) Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound. IEEE Trans Med Imaging 15(4):466–478

    Article  Google Scholar 

  14. Lee WL, Chen YC, Hsieh KS (2003) Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform. IEEE Trans Med Imaging 22(3):382–392

    Article  Google Scholar 

  15. Sujana H, Swarnamani S, Suresh S (1996) Application of artificial neural networks for the classification of liver lesions by image texture parameters. Ultrasound Med Biol 22(2):1177–1181

    Article  Google Scholar 

  16. Virmani J, Kumar V, Kalra N, Khandelwal N (2013) A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound. J Med Eng Technol 37(4):292–306

    Article  Google Scholar 

  17. Virmani J, Kumar V, Kalra N, Khandelwal N (2013) Characterization of primary and secondary malignant liver lesions from B-mode ultrasound. J Digit Imaging 26(6):1058–1070

    Article  Google Scholar 

  18. Virmani J, Kumar V, Kalra N, Khandelwal N (2013) PCA-SVM based CAD system for focal liver lesions using B-mode ultrasound images. Def Sci J 64(5):478–486

    Article  Google Scholar 

  19. Virmani J, Kumar V, Kalra N, Khandelwal N (2013) Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound. In J Converg Comput 1(1):19–37

    Google Scholar 

  20. Virmani J, Kumar V, Kalra N, Khandelwal N (2013) SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors. J Digit Imaging 26(3):530–543

    Article  Google Scholar 

  21. Virmani J, Kumar V, Kalra N, Khandelwal N (2014) Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound. J Digit Imaging 27(4):520–537

    Article  Google Scholar 

  22. Xian GM (2010) An identification of malignant and benign liver tumors form ultrasonography based on GLCM texture features and fuzzy SVM. Expert Syst Appl 37(10):6737–6741

    Article  MathSciNet  Google Scholar 

  23. Yoshida H, Casalino DD, Keserci B, Coskun A, Ozturk O, Savranlar A (2003) Wavelet-packet based texture analysis for differentiation between benign and malignant liver tumors in ultrasound images. Phys Med Biol 48(22):3735–3753

    Article  Google Scholar 

  24. Attia MW, Moustafa HD, Abou-Chadi FEZ, Mekky N (2015) Classification of ultrasound kidney images using PCA and neural networks. Int J Adv Comput Sci Appl 6(4):53–57

    Google Scholar 

  25. Raja KB, Madheswaran M, Thyagarajah KJ (2007) Quantitative and qualitative evaluation of US kidney images for disorder classification using multi-scale differential features. ICGST-BIME J 7(1):1–8

    Google Scholar 

  26. Raja KB, Madheswaran M, Thyagarajah KJ (2007) Ultrasound kidney image analysis for computerized disorder identification and classification using content descriptive power spectral features. J Med Syst 31(5):307–317

    Article  Google Scholar 

  27. Raja KB, Madheswaran M, Thyagarajah KJ (2008) A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features. J Med Syst 32(1):65–83

    Article  Google Scholar 

  28. Sharma K, Virmani J (2017) A decision support system for classification of normal and medical renal disease using ultrasound images: a decision support system for medical renal diseases. Int J Ambient Comput Intell 8(2):52–69

    Article  Google Scholar 

  29. Sharma K, Virmani J (2017) Haralick’s texture descriptors for classification of renal ultrasound images. In: Bhattacharyya S, Mukherjee A, Pan I, Dutta P, Bhaumik AK (eds) Hybrid intelligent techniques for pattern analysis and understanding. CRC Press, Boca Raton, pp 277–309

    Chapter  Google Scholar 

  30. Braeckman J, Autier P, Garbar C, Marichal MP, Soviany C, Nir R, Michielsen D, Bleiberg H, Egevad L, Emberton M (2007) Computer-aided ultrasonography (HistoScanning): a novel technology for locating and characterizing prostate cancer. BJU Int 101(3):293–298

    Article  Google Scholar 

  31. Han SM, Lee HK, Choi JY (2008) Computer-aided prostate cancer detection using texture features and clinical features in ultrasound images. J Digit Imaging 21(Suppl 1):121–133

    Article  Google Scholar 

  32. Huynen AL, Giesen RJB, de la Rosette JJMCH, Aernink RG, Debruyne FMJ, Wijkstra H (1994) Analysis of ultrasonographic prostate images for the detection of prostatic carcinoma: the automated urologic diagnostic expert system. Ultrasound Med Biol 20(1):1–10

    Article  Google Scholar 

  33. Llobet R, Perez-Cortes JC, Toselli AH, Juan A (2007) Computer-aided detection of prostate cancer. Int J Med Inform 76(7):547–556

    Article  Google Scholar 

  34. Mohamed SS, Salama MMA (2005) Computer-aided diagnosis for prostate cancer using support vector machine. Proc. SPIE 5744, Medical imaging 2005: visualization, image-guided procedures and display (12 April 2005). https://doi.org/10.1117/12.598800

  35. Mohamed SS, Salama MMA, Kamel M, El-Saadany EF, Rizkalla R, Chin J (2005) Prostate cancer multi-feature analysis using trans-rectal ultrasound images. Phys Med Biol 50(15):N175. https://doi.org/10.1088/0031-9155/50/15/N02

    Article  Google Scholar 

  36. Moradi M, Abolmaesumi P, Siemens DR, Sauerbrei EE, Boag AH, Mousavi P (2009) Augmenting detection of prostate cancer in trans-rectal ultrasound images using SVM and FR time series. IEEE Trans Biomed Eng 56(9):2214–2224

    Article  Google Scholar 

  37. Scheipers U, Ermert H, Sommerfeld HJ, Schurmann MG, Senge T, Philippou S (2003) Ultrasonic multifeature tissue characterization for prostate diagnosis. Ultrasound Med Biol 29(8):1137–1149

    Article  Google Scholar 

  38. http://ultrasoundcases.info/category.aspx?cat=67. Accessed Dec 2016

  39. Medical images, available at http://www.onlinemedicalimages.com. Accessed Mar 2019

  40. Rodrigues PS (2017) Breast ultrasound image. Mendeley Data, v1, doi: https:// dx.doi.org/https://doi.org/10.17632/wmy84gzngw.1

  41. Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2020) Dataset of breast ultrasound images. Data Brief. https://doi.org/10.1016/j.dib.2019.104863

    Article  Google Scholar 

  42. Loizou CP, Pattichis CS, Christodoulou CI, Istepanian RSH, Pantziaris M, Nicolaides A (2005) Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery. IEEE Trans Ultrason Ferroelectr Freq Control 52(10):1653–1669

    Article  Google Scholar 

  43. Loizou CP, Theofanous C, Pantziaris M, Kasparis T (2014) Despeckle filtering software toolbox for ultrasound imaging of common carotid artery. Comput Methods Progr Biomed 114(1):109–124

    Article  Google Scholar 

  44. Rafati M, Arabfard M, Zadeh MRR, Maghsoudloo M (2016) Assessment of noise reduction in ultrasound images of common carotid and brachial arteries. IET Digit Libr 10(1):1–6

    Google Scholar 

  45. Adam D, Beilin Nissan S, Friedman Z, Behar V (2006) The combined effect of spatial compounding and nonlinear filtering on the speckle reduction in ultrasound images. Ultrasonics 44(2):166–181

    Article  Google Scholar 

  46. Hafizah WM, Supriyanto E (2011) Comparative evaluation of ultrasound kidney image enhancement techniques. Int J Comput Appl 21(7):15–19

    Google Scholar 

  47. Hiremath PS, Akkasaligar PT, Badiger S (2010) Visual enhancement of digital ultrasound images using multiscale wavelet domain. Pattern Recogt Image Anal 20(3):303–315

    Article  Google Scholar 

  48. Hiremath PS, Akkasaligar PT, Badiger S (2011) Performance comparison of wavelet transform and contourlet transform based methods for despeckling medical ultrasound images. Int J Comput Appl 26(9):34–41

    Google Scholar 

  49. Hiremath PS, Akkasaligar PT, Badiger S (2011) Speckle reducing contourlet transform for medical ultrasound images. Int J Comput Inf Eng 5(8):932–939

    Google Scholar 

  50. Rahman T, Uddin MS (2013) Speckled noise reduction and segmentation of kidney regions from ultrasound image. Proc Int Conf Inf Electron Vis. https://doi.org/10.1109/ICIEV.2013.6572601

    Article  Google Scholar 

  51. Subramanya MB, Kumar V, Mukherjee S, Saini M (2015) SVM-based CAC system for B-mode kidney ultrasound images. J Digit Imaging 28(4):448–458

    Article  Google Scholar 

  52. Abrahim BA, Kadah Y (2011) Speckle noise reduction method combining total variation and wavelet shrinkage for clinical ultrasound imaging. In: Proceedings of 1st middle eastern conference on biomedical engineering, Sharjah, UAE 21-24 Feb 2011. https://doi.org/10.1109/MECBME.2011.5752070

  53. Gupta D, Anand RS, Tyagi B (2015) Despeckling of ultrasound medical images using ripplet domain and nonlinear filtering. SIViP 9(5):1093–1111

    Article  Google Scholar 

  54. Gupta D, Anand RS, Tyagi B (2015) Speckle filtering of ultrasound images using a modified non-linear diffusion model in non-subsampled shearlet domain. IET Image Proc 9(2):107–117

    Article  Google Scholar 

  55. Manth N, Virmani J, Kumar V, Kalra N, Khandelwal N (2015) Despeckle filtering: performance evaluation for malignant focal hepatic lesions. In: Proceedings of 2nd International conference on computing for sustainable global development (INDIACom), New Delhi, India, 11-13 March 2015, pp. 1897–1902

  56. Vanithamani R, Umanaheswari G (2010) Performance analysis of filters for speckle reduction in medical ultrasound images. Int J Comput Appl 12(6):23–27

    Google Scholar 

  57. Gupta D, Anand RS, Tyagi B (2014) Ripplet domain non-linear filtering for speckle reduction in ultrasound medical images. Biomed Signal Process Control 10(1):79–91

    Article  Google Scholar 

  58. Wong A, Scharcanski J (2012) Monte Carlo despeckling of transrectal ultrasound images of the prostate. Digit Signal Process 22(5):768–775

    Article  MathSciNet  Google Scholar 

  59. Biradar N, Dewal ML, Rohit MK (2014) A novel hybrid homomorphic fuzzy filter for speckle noise reduction. Biomed Eng Lett 4(2):176–185

    Article  Google Scholar 

  60. Biradar N, Dewal ML, Rohit MK (2014) Edge preserved speckle noise reduction using integrated fuzzy filters. Int Sch Res Not 2014:1–11

    Google Scholar 

  61. Biradar N, Dewal ML, Rohit MK (2015) Speckle noise reduction in B-mode echocardiographic images: a comparison. IETE Tech Rev 32(6):435–453

    Article  Google Scholar 

  62. Biradar N, Dewal ML, Rohit MK, Gowre S, Gundge Y (2016) Blind source parameters for performance evaluation of despeckling filters. Int J Biomed Imaging 2016:1–12

    Article  Google Scholar 

  63. Drukker K, Gruszauskas NP, Giger ML (2009) Principal component analysis, classifier complexity and robustness of sonographic breast lesion classification. Proc. SPIE 7260, Medical imaging 2009: computer-aided diagnosis, 72602B (3 March 2009). https://doi.org/10.1117/12.811341

  64. Drukker K, Sennett CA, Giger ML (2009) Automated method for improving system performance of computer-aided diagnosis in breast ultrasound. IEEE Trans Med Imaging 28(1):122–128

    Article  Google Scholar 

  65. Horsch K, Giger ML, Venta LA, Vyborny CJ (2002) Computerized diagnosis of breast lesions on ultrasound. Med Phys 29(2):157–164

    Article  Google Scholar 

  66. Menon RV, Raha P, Kothari S, Chakraborty S, Chakrabarti I, Karim R (2015) Automated detection and classification of mass from breast ultrasound images. In: Proceedings of 5th national conference on computer vision, pattern recognition, image processing and graphics, Patna, India, 16-19 Dec 2015. https://doi.org/10.1109/NCVPRIPG.2015.7490070

  67. Raha P, Menon RV, Chakrabarti I (2017) Fully automated computer aided diagnosis system for classification of breast mass from ultrasound images. In: Proceedings of 2017 international conference on wireless communications, signal processing and networking (WiSPNET), Chennai, India, 22-24 March 2017, pp. 48–51

  68. Wu WJ, Lin SW, Moon WK (2012) Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images. Comput Med Imaging Gr 36(8):627–633

    Article  Google Scholar 

  69. Wu WJ, Moon WK (2008) Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features. Acad Radiol 15(7):873–880

    Article  Google Scholar 

  70. Flores WG, de Albuquerque Pereira WC, Infantosi AFC (2015) Improving classification performance of breast lesions on ultrasonography. Pattern Recognit 48(4):1125–1136

    Article  Google Scholar 

  71. Nemat H, Fehri H, Ahmadinejad N, Frangi AF, Gooya A (2018) Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features. Med Phys 45(9):4112–4124

    Article  Google Scholar 

  72. Rodriguez-Cristerna A, Gomez-Flores W, de Albuquerque Pereira WC (2017) A computer-aided diagnosis for breast ultrasound based on weighted BI-RADS classes. Comput Methods Programs Biomed 153:33–40

    Article  Google Scholar 

  73. Uzunhisarcikli E, Goreke V (2018) A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system. Sadhana 43(9):138

    Article  MathSciNet  MATH  Google Scholar 

  74. Amin KM, Shahin AI, Guo Y (2016) A novel breast tumor classification algorithm using neutrosophic score features. Measurement 81:210–220

    Article  Google Scholar 

  75. Daoud MI, Bdair TM, Al-Najar M, Alazral R (2016) A fusion based approach for breast ultrasound image classification using multiple-ROI texture and morphological analyses. Comput Math Methods Med 2016:1–12

    Article  MATH  Google Scholar 

  76. Singh BK, Verma K, Thoke AS (2015) Adaptive gradient descent backpropagation for classification of breast tumors in ultrasound imaging. Proc Comput Sci 46:1601–1609

    Article  Google Scholar 

  77. Singh BK, Verma K, Thoke AS, Suri JS (2017) Risk stratification of 2D ultrasound-based breast lesions using hybrid feature selection in machine learning paradigm. Measurement 105:146–157

    Article  Google Scholar 

  78. Verma K, Singh BK, Tripathi P, Thoke AS (2015) Review of feature selection algorithms for breast cancer ultrasound images. In: Barbucha D et al (eds) New Trends in intelligent information and database systems, Springer, Cham, vol. 598, pp. 23–32

  79. Wang Y, Shen J, Guo, Y Wang W (2008) Computerized classification of breast tumors with morphological and texture features of ultrasonic images. In: Proceedings of 21st IEEE international symposium on computer-based medical systems, Jyvaskyla, Finland, 17-19 June 2008, pp. 23–28

  80. Yap MH, Edirisinghe EA, Bez HE (2009) A comparative study in ultrasound breast imaging classification. Proc. SPIE 7259, Medical Imaging 2009: Image processing, 72591S, (27 March 2009). https://doi.org/10.1117/12.811208

  81. Bhusri S, Jain S, Virmani J (2016) Breast lesion classification using the amalgamation of morphological and texture features. Int J Pharm Bio Sci 7(2):617–624

    Google Scholar 

  82. Lee JH, Seong YK, Chang CH, Ko EY, Cho BH, Ku J, Woo KG (2013) Computer aided lesion diagnosis in B-mode ultrasound by border irregularity and multiple sonographic features. Proc. SPIE 8670, Medical imaging 2013: computer-aided diagnosis, 86701O (28 Feb 2013). https://doi.org/10.1117/12.2007452

  83. Shen WC, Chang RF, Moon WK (2007) Computer aided classification system for breast ultrasound based on breast imaging reporting and data system (BI-RADS). Ultrasound Med Biol 33(11):1688–1698

    Article  Google Scholar 

  84. Shen WC, Chang RF, Moon WK, Chou YH, Huang CS (2007) Breast ultrasound computer-aided diagnosis using BI-RADS features. Acad Radiol 14(8):928–939

    Article  Google Scholar 

  85. Takemura A, Shimizu A, Hamamoto K (2010) Discrimination of breast tumors in ultrasonic images using an ensemble classifier based on the AdaBoost algorithm with feature selection. IEEE Trans Med Imaging 29(3):598–609

    Article  Google Scholar 

  86. Drukker K, Edwards DC, Giger ML, Nishikawa RM, Metz CE (2004) Computerized detection and 3-way classification of breast lesions on ultrasound images. Proc. SPIE 5370, Medical imaging 2004: image processing (12 May 2004). https://doi.org/10.1117/12.534339

  87. Prabusankarlal KM, Thirumoorthy P, Manavalan R (2015) Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound. HCIS 5:12–28

    Google Scholar 

  88. Cui J, Sahiner B, Chan HP, Nees A, Paramagul C, Hadjiiski LM, Zhou C, Shi J (2009) A new automated method for the segmentation and characterization of breast masses on ultrasound images. Med Phys 36(5):1553–1565

    Article  Google Scholar 

  89. Cui J, Sahiner B, Chan HP, Shi J, Nees A, Paramagul C, Hadjiiski LM (2009) A computer-aided diagnosis system for prediction of the probability of malignancy of breast masses on ultrasound images. Proc. SPIE 7260, Medical imaging 2009: computer-aided diagnosis, 72600L (3  March 2009). https://doi.org/10.1117/12.813722

  90. Huang YL (2009) Computer-aided diagnosis using neural networks and support vector machines for breast ultrasonography. J Med Ultrasound 17(1):17–24

    Article  Google Scholar 

  91. Kriti, Virmani J, Agarwal R (2019) Effect of despeckle filtering on classification of breast tumors using ultrasound images. Biocybern Biomed Eng 39(2):536–560

    Article  Google Scholar 

  92. Moon WK, Chen IL, Yi A, Bae MS, Shin SU, Chang RF (2018) Computer-aided prediction model for axillary lymph node metastasis in breast cancer using tumor morphological and textural features on ultrasound. Comput Methods Progr Biomed 162:129–137

    Article  Google Scholar 

  93. Moon WK, Huang YS, Lo CM, Huang CS, Bae MS, Kim WH, Chen JH, Chang RF (2015) Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features. Med Phys 42(6):3024–3035

    Article  Google Scholar 

  94. Moon WK, Lo CM, Chang JM, Huang CS, Chen JH, Chang RF (2013) Quantitative ultrasound analysis for classification of BI-RADS category 3 breast masses. J Digit Imaging 26(6):1091–1098

    Article  Google Scholar 

  95. Moon WK, Lo CM, Cho N, Chang JM, Huang CS, Chen JH, Chang RF (2013) Computer-aided diagnosis of breast masses using quantified BI-RADS findings. Comput Methods Progr Biomed 111(1):84–92

    Article  Google Scholar 

  96. Nugroho HA, Yusufiyah HKN, Adji TB, Nugroho A (2015) Zernike moments feature extraction for classifying lesion’s shape of breast ultrasound images. In: Proceedings of 7th international conference on information technology and electrical engineering, Chiang  Mai, Thailand, 29-30 Oct. 2015, pp. 458–463

  97. Wu WJ, Lin SW, Moon WK (2015) An artificial immune system-based support vector machine approach for classifying ultrasound breast tumor images. J Digit Imaging 28(5):576–585

    Article  Google Scholar 

  98. Yusufiyah HKN, Nugroho HA, Adji TB, Nugroho A (2015) Feature extraction for classifying lesion’s shape, of breast ultrasound images. In: Proceedings of 2nd international conference on information technology, computer and electrical engineering, Semarang, Indonesia, 16-18 Oct. 2015, pp. 102–106

  99. Zakeri FS, Behnam H, Ahmadinejad N (2012) Classification of benign and malignant breast masses based on shape and texture features in sonography images. J Med Syst 36(3):1621–1627

    Article  Google Scholar 

  100. Karimi B, Krzyzak A (2014) Computer-aided system for automatic classification of suspicious lesions in breast ultrasound images. In: Rutkowski L, Korytowski M, Scherer R, Tadeusiewicz R, Zadeh LA, Zurada JM (eds) Artificial Intelligence and Soft Computing, Springer, Cham, vol. 8468, pp. 131–142

  101. Huang Y, Han L, Dou H, Luo H, Yuan Z, Zhang J, Fin G (2019) Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images. Biomed Eng Online. https://doi.org/10.1186/s12938-019-0626-5

    Article  Google Scholar 

  102. Xie X, Shi F, Niu J, Tang X (2018) Breast ultrasound image classification and segmentation using convolutional neural networks. In: Hong R et al (eds) Pacific rim conference on multimedia. Springer, Berlin, pp 200–211

    Google Scholar 

  103. Byra M, Nowicki A, Wroblewska-Piotrzkowska H, Dobruch-Sobczak K (2016) Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters. Med Phys 43(10):5561–5569

    Article  Google Scholar 

  104. Destrempes F, Trop I, Alard L, Chayer B, Garci-Duitama J, El Khoury M, Lalonde L, Cloutier G (2020) Added value of quantitative ultrasound and machine learning in BI-RADS 4–5 assessment of solid breast lesions. Ultrasound Med Biol 46(2):436–444

    Article  Google Scholar 

  105. Dobruch-Sobczak K, Piotrzkowska Wroblewska H, Roszkowska-Purska K, Nowicki A, Jakubowski W (2017) Usefulness of combined BI-RADS analysis and Nakagami statistics of ultrasound echoes in the diagnosis of breast lesions. Clin Radiol 72(4):339.e7-339.e15

    Article  Google Scholar 

  106. Hsu SM, Kuo WH, Kuo FC, Liao YY (2019) Breast tumor classification using different features of quantitative ultrasound parametric images. Int J Comput Assist Radiol Surg 14(4):623–633

    Article  Google Scholar 

  107. Liao CH, Li CH, Tsui PH, Chang CC, Kuo WH, Chang KJ, Yeh CK (2012) Strain-compounding technique with ultrasound Nakagami imaging for distinguishing between benign and malignant breast tumors. Med Phys 39(5):2325–2333

    Article  Google Scholar 

  108. Molthen RC, Shankar PM, Reid JM, Forsberg F, Halpern EJ, Piccoli CW, Goldberg BB (1998) Comparisons of the Rayleigh and K-distribution models using in vivo breast and liver tissue. Ultrasound Med Biol 24(1):93–100

    Article  Google Scholar 

  109. Shankar PM, Dumane VA, George T, Piccoli CW, Reid JM, Forseberg F, Goldberg BB (2003) Classification of breast masses in ultrasonic B scans using Nakagami and K distributions. Phys Med Biol 48(14):2229–2240

    Article  Google Scholar 

  110. Shankar PM, Dumane VA, Piccoli CW, Reid JM, Forsberg F, Goldberg BB (2002) Classification of breast masses in ultrasonic B-mode images using a compounding technique in the Nakagami distribution domain. Ultrasound Med Biol 28(10):1295–1300

    Article  Google Scholar 

  111. Shankar PM, Dumane VA, Reid JM, Genis V, Forsberg F, Piccoli CW, Goldberg BB (2001) Classification of ultrasonic B-mode images of breast masses using Nakagami distribution. IEEE Trans Ultrason Ferroelectr Freq Control 48(2):569–580

    Article  Google Scholar 

  112. Tsui PH, Yeh CK, Chang CC, Liao YY (2008) Classification of breast masses by ultrasonic Nakagami imaging: a feasibility study. Phys Med Biol 53(21):6027–6044

    Article  Google Scholar 

  113. Liao YY, Tsui PH, Li CH, Chang KJ, Kuo WH, Chang CC, Yeh CK (2011) Classification of scattering media within benign and malignant breast tumors on ultrasound texture-feature-based and Nakagami parameter images. Med Phys 38(4):2198–2207

    Article  Google Scholar 

  114. Lefebvre F, Meunier M, Thibault F, Laugier P, Berger G (2000) Computerized ultrasound B-scan characterization of breast nodules. Ultrasound Med Biol 26(9):1421–1428

    Article  Google Scholar 

  115. Moon WK, Lo CM, Huang CS, Chen JH, Chang RF (2012) Computer-aided diagnosis based on speckle patterns in ultrasound images. Ultrasound Med Biol 38(7):1251–1261

    Article  Google Scholar 

  116. Piliouras N, Kalatzis I, Dimitropolous N, Cavouras D (2004) Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound. Comput Med Imaging Gr 28(5):247–255

    Article  Google Scholar 

  117. Su Y, Wang Y, Jiao J, Guo Y (2011) Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features. Open Med Inform J 5(Suppl1-M3):26–37

    Article  Google Scholar 

  118. Alvarenga AV, Infantosi AFC, Pereira WCA, Azevedo CM (2012) Assessing the combined performance of texture and morphological parameters in distinguishing breast tumors in ultrasound images. Med Phys 39(12):7350–7358

    Article  Google Scholar 

  119. Chen CY, Chiou HJ, Chou SY, Chiou SY, Wang HK, Chou YH, Chiang HK (2009) Computer-aided diagnosis of soft-tissue tumors using sonographic morphologic and texture features. Acad Radiol 16(2):1531–1538

    Article  Google Scholar 

  120. Kim K, Song MK, Kim EK, Yoon JH (2017) Clinical application of S-detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography 36(1):3–9

    Article  Google Scholar 

  121. Prabusankarlal KM, Thirumoorthy P, Manavalan R (2017) Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection. J Med Imaging 4(2):024507. https://doi.org/10.1117/1.JMI.4.2.024507

    Article  Google Scholar 

  122. Ruggiero C, Bagnoli F, Sacile R, Calabrese M, Rescinito G, Sardanelli F (1998) Automatic recognition of malignant lesions in ultrasound images by artificial neural networks. In: Proceedings of 20th annual international conference of the ieee engineering in medicine and biology society. Vol. 20 biomeical engineering towards the year 2000 and beyond, Hong Kong, China, 1 Nov. 1998, pp. 872–875

  123. Laws KI (1979) Texture energy measures. In: Proceedings of image understanding workshop, pp. 47–51

  124. Acharya UR, Meiburger KM, Koh JEW, Ciaccio EJ, Arunkumar N, See MH, Taib NAM, Vijayananthan A, Rahmat K, Fadzli F, Leong SS, Westerhout CJ, Astaiza AC, Gonzalez GR (2019) A novel algorithm for breast lesion detection using textons and local configuration pattern features with ultrasound imagery. IEEE Access 7:22829–22842

    Article  Google Scholar 

  125. Matsumoto MMS, Sehgal CM, Udupa JK (2012) Local binary pattern texture-based classification of solid masses in ultrasound breast images. Proc. SPIE 8320, Medical imaging 2012: ultrasonic imaging, tomography and therapy,  83201H (25 Feb 2012). https://doi.org/10.1117/12.911653

  126. Liao R, Wan T, Qin Z (2011) Classification of benign and malignant breast tumors in ultrasound images based on multiple sonographic and textural features. In: Proceedings of 3rd international conference on intelligent human-machine systems and cybernetics, Hangzhou, China, 26-27 Aug. 2011, pp. 71–74

  127. Zhang Q, Chang H, Liu L, Li A, Huang Q (2014) A computer aided system for classification of breast tumors in ultrasound images via biclustering learning. In: Wang X, Pedrycz W, Chan P, He Q (eds) Machine learning and cybernetics, Springer, Berlin, Hiedelberg, pp. 24–32

  128. Drukker K, Giger ML, Vyborny CJ, Schmidt RA, Mendelson EB, Stern M (2003) Computerized detection and classification of lesions on breast ultrasound. Proc. SPIE 5032, Medical Imaging 2003: image processing (15May 2003). https://doi.org/10.1117/12.480856

  129. Drukker K, Gruszauskas NP, Sennett CA, Giger ML (2008) Breast US computer-aided diagnosis workstation: performance with a large clinical diagnostic population. Radiology 248(2):392–397

    Article  Google Scholar 

  130. Shan J, Alam SK, Garra B, Zhang Y, Ahmed T (2015) Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods. Ultrasound Med Biol 42(4):980–988

    Article  Google Scholar 

  131. Hussain Z, Gimenez F, Yi D, Rubin D (2018). Differential data augmentation techniques for medical image classification tasks. In: Proceedings of AMIA annual symposium proceedings archive, pp. 979–984

  132. Mikolajczyk A, Grochowski M (2018) Data augmentation for improving deep learning in image classification problem. In: Proceedings of 2018 international interdisciplinary PhD workshop, Poland, 9-12 May 2018, pp. 117–122

  133. Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. arXiv:1712.04621v1

  134. Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, Huang CS, Shen D, Chen CM (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT. Sci Rep 6(1):1–13

    Google Scholar 

  135. Lee CY, Chen GL, Zhang ZX, Chou YH, Hsu CC (2018) Is intensity inhomogeneity correction useful for classification of breast cancer in sonograms using deep neural network ? J Healthc Eng 2018:1–10

    Article  Google Scholar 

  136. Zhang E, Seiler S, Chen M, Lu W, Gu X (2020) BIRADS features oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis. Phys Med Biol 65(12):125005

    Article  Google Scholar 

  137. Al-Garadi MA, Mohammed A, Al-Ali A, Du X, Guizani M (2018) A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun Surv Tutor 22(3):1646–1685

  138. Druzhkov PN, Kustikova VD (2016) A survey of deep learning methods and software tools for image classification and object detection. Pattern Recognit Image Anal 26(1):9–15

    Article  Google Scholar 

  139. Hu Z, Tang J, Wang Z, Zhang K, Zhang L, Sun Q (2018) Deep learning for image-based cancer detection and diagnosis- a survey. Pattern Recognit 83:134–149

    Article  Google Scholar 

  140. Shen D, Wu G, Suk HL (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248

    Article  Google Scholar 

  141. Sornam M, Muthusubash K, Vanitha V (2017) A survey on image classification and activity recognition using deep convolutional neural architectures. In: Proceedings of 9th international conference on advanced computing, Chennai, India, 14-16 Dec 2017, pp. 121–126

  142. Meyer P, Noblet V, Mazzara C, Lallement A (2018) Survey on deep learning for radiotherapy. Comput Med Biol 98:124–146

    Article  Google Scholar 

  143. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge

    MATH  Google Scholar 

  144. Zhou SK, Greenspan H, Shen D (eds) (2017) Deep learning for medical image analysis. Academic Press, Cambridge

    Google Scholar 

  145. Rawat W, Wang Z (2017) Deep convolutional neural network for image classification: a comprehensive review. Neural Comput 29(9):2352–2449

    Article  MathSciNet  MATH  Google Scholar 

  146. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van der Laak JA, Van Ginneken B, Sanchez CI (2017) A survey of deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  147. Weiss K, Khoshgoftaar TM, Wang DD (2016) A survey of transfer learning. J Big Data. https://doi.org/10.1186/s40537-016-0043-6

    Article  Google Scholar 

  148. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

    MathSciNet  MATH  Google Scholar 

  149. Wan L, Zeiler M, Zhang S, LeCun Y, Fergus R (2013) Regularization of neural networks using DropConnect. In: Proceedings of 30th international conference on machine learning 28(3):1058–1066

  150. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of 32nd international conference on machine learning 37:448–456

  151. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of 31st AAAI conference on artificial intelligence, pp. 4278–4284

  152. Xie S, Girshick R, Dollar P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 1492–1500

  153. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Article  MathSciNet  Google Scholar 

  154. Metz CE (1978) Basic principles of ROC analysis. Semin Nucl Med 8(4):283–298

    Article  Google Scholar 

  155. Long Z, Chen L, Jiang C, Zhang L, Alzheimer’s disease neuroimaging initiative (2017) Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS ONE 12(3):e0173372. https://doi.org/10.1371/journal.pone.0173372

    Article  Google Scholar 

  156. Karimi B, Krzyzak A (2013) A novel approach for automatic detection and classification of suspicious lesions in breast ultrasound images. JAISCR 3(3):265–276

    Google Scholar 

  157. Kriti, Virmani J, Agarwal R (2019) Assessment of despeckle filtering algorithms forsegmentation of breast tumors from ultrasound images. Biocybern Biomed Eng 39(1):100–121

    Article  Google Scholar 

  158. Han S, Kang HK, Jeong JY, Park MH, Kim W, Bang WC, Seong YK (2017) A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol 62(19):7714–7728

    Article  Google Scholar 

  159. Xiao T, Liu L, Li K, Qin W, Yu S, Li Z (2018) Comparison of transferred deep neural networks in ultrasonic breast masses discrimination. Biomed Res Int. https://doi.org/10.1155/2018/4605191

    Article  Google Scholar 

  160. Cao Z, Duan L, Yang G, Yue T, Chen Q (2019) An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures. BMC Med Imaging 19(1):51. https://doi.org/10.1186/s12880-019-0349-x

    Article  Google Scholar 

  161. Fujioka T, Kubota K, Mori M, Kikuchi Y, Katsuta L, Kasahara M, Oda G, Ishiba T, Nakagawa T, Tateishi U (2019) Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network. Jpn J Radiol 37:466–472

    Article  Google Scholar 

  162. Tanaka H, Chiu SW, Watanabe T, Kaoku S, Yamaguchi T (2019) Computer-aided diagnosis system for breast ultrasound images using deep learning. Phys Med Biol 64(23):235013. https://doi.org/10.1088/1361-6560/ab5093

    Article  Google Scholar 

  163. Moon WK, Lee YW, Ke HH, Lee SH, Huang CS, Chang RF (2020) Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Comput Methods Progr Biomed 190:105361

    Article  Google Scholar 

  164. Zeimarani B, Costa MGF, Nurani NZ, Filho CFFC (2019) A novel breast tumor classification in ultrasound images, using deep convolutional neural network. In: Costa RF, Machado J, Alvarenga A (eds) XXVI Brazilian congress on biomedical engineering, Springer, Singapore, vol. 70/2, pp. 89–94

  165. Kameswari SSD, Vijayakumar V (2019) A CNN based breast tumor classifier using Mendeley BUS dataset. Int J Innov Technol Explor Eng 8(6):1340–1343

    Google Scholar 

  166. Qi X, Zhang L, Chen Y, Pi Y, Chen Y, Lv Q, Yi Z (2019) Automated diagnosis of breast ultrasonography images using deep learning neural networks. Med Image Anal 52:185–198

    Article  Google Scholar 

  167. Zeimarani B, Costa MGF, Nurani NZ, Bianco SR, Pereira WCA, Filho CFFC (2019) Breast lesion classification in ultrasound images using deep convolutional neural network. IEEE Access 8:133349–1333359

    Article  Google Scholar 

  168. Becker AS, Mueller M, Stoffel E, Marcon M, Ghafoor S, Boss A (2018) Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study. Br J Radiol 91(1083):21070576. https://doi.org/10.1259/bjr.20170576

    Article  Google Scholar 

  169. Stoffel E, Becker AS, Wurnig MC, Marcon M, Ghafoor S, Berger N, Boss A (2018) Distinction between phyllodes tumor and fibroadenoma in breast ultrasound using deep learning image analysis. Eur J Radiol 5:165–170

    Article  Google Scholar 

  170. Zhao C, Xiao M, Jiang Y, Liu H, Wang M, Wang H, Sun Q, Zhu Q (2019) Feasibility of computer-assisted diagnosis for breast ultrasound: the results of the diagnostic performance of S-detect form a single center in China. Cancer Manag Res 11:921–930

    Article  Google Scholar 

  171. Chang YW, Chen YR, Ko CC, Lin WY, Lin KP (2020) A novel computer-aided-diagnosis system for breast ultrasound images based on BI-RADS categories. Appl Sci 10(5):1830

    Article  Google Scholar 

  172. Ciritsis A, Rossi C, Eberhard M, Marcon M, Becker AS, Boss A (2019) Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making. Eur Radiol 29(10):5458–5468

    Article  Google Scholar 

  173. Zhuang Z, Kang Y, Raj ANJ, Yuan Y, Ding W, Qiu S (2020) Breast ultrasound lesion classification based on image decomposition and transfer learning. Med Phys 47(12):6257–6269

    Article  Google Scholar 

  174. Byra M (2018) Discriminant analysis of neural style representations for breast lesion classification in ultrasound. Biocybern Biomed Eng 38(3):684–690

    Article  Google Scholar 

  175. Daoud MI, Abdel-Rahman S, Bdair TM, Al-Najar MS, Al-Hawari FH, Alazrai R (2020) Breast tumor classification in ultrasound images using combined deep and handcrafted features. Sensors 20(23):6838. https://doi.org/10.3390/s20236838

    Article  Google Scholar 

  176. Byra M, Galperin M, Ojeda-Fournier H, Olson L, O’Boyle M, Comstock C, Andre M (2019) Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion. Med Phys 46(2):746–755

    Article  Google Scholar 

  177. Kriti, Virmani J, Agarwal R (2020) Deep feature extraction and classification of breast ultrasound images. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09337-z

    Article  Google Scholar 

  178. Bergstra J, Bardenet R, Bengio Y, Kegl B (2011) Algorithms for hyper-parameter optimization. Adv Neural Info Proc Syst 24:2546–2554

  179. Talathi SS (2015) Hyper-parameter optimization for deep convolutional networks for object recognition. In: Proceedings of IEEE international conference on image processing, Quebec City, Canada, 27-30 Sept. 2015, pp. 3982–3986

  180. Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, Schilling B, Haferkamp S, Schadendorf D, Letz TH, Utikal JS, von Kalle C (2019) Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer 113:47–54

    Article  Google Scholar 

  181. Lorenzo PR, Nalepa J, Kawulok M, Ramos LS, Pastor JR (2017) Particle swarm optimization for hyper-parameter selection in deep neural networks. In: Proceedings of the genetic and evolutionary computation conference, Berlin, Germany, 15-19 July 2017, pp. 481–488

  182. Zhang X, Chen X, Yao L, Ge C, Dong M (2019) Deep neural network hyperparameter optimization with orthogonal array tuning. In: Gedeon T, Wong K, Lee M (Eds.), Neural information processing, Springer Cham, pp. 287–295

  183. Young SR, Rose DC, Karnowski TP, Lim SH, Patton RM (2015) Optimizing deep learning hyper-parameters through evolutionary algorithm. In: Proceedings of the workshop on machine learning in high-performance computing environments, Austin, Texas, 15 Nov. 2015 pp. 1–5

  184. Huang Q, Zhang F, Li X (2018) Machine learning in ultrasound computer-aided diagnostic systems: a survey. BioMed Res Int. https://doi.org/10.1155/2018/5137904

    Article  Google Scholar 

  185. Wu GG, Zhou LQ, Xu JW, Wang JY, Wei Q, Deng YB, Cui XW, Dietrich CF (2019) Artificial intelligence in breast ultrasound. World J Radiol 11(2):19–26

    Article  Google Scholar 

  186. Han S, Lee S, Lee JR (2019) A practical implementation of deep learning method for supporting the classification of breast lesions in ultrasound images. Int J Adv Smart Converg 8(1):24–34

    MathSciNet  Google Scholar 

  187. Gruszauskas NP, Drukker K, Giger ML, Sennett CA, Pesce LL (2008) Performance of breast ultrasound computer-aided diagnosis dependence on image selection. Acad Radiol 15(10):1234–1245

    Article  Google Scholar 

  188. Guan H, Zhang Y, Cheng HG, Tang X (2020) Bounded–abstaining classification for breast tumors in imbalanced ultrasound images. Int J Appl Math Comput Sci 30(2):325–336

    MathSciNet  MATH  Google Scholar 

  189. Al-Dhabyani W, Fahmy A, Gomma M, Khaled H (2019) Deep learning approaches for data augmentation and classification of breast masses using ultrasound images. Int J Adv Comput Sci Appl 10(5):618–627

    Google Scholar 

  190. Yousef Kalaf E, Jodeiri A, Kamaledin Setarehdan S, Lin NW, Rahman KB, Aishah Taib N, Dhillon SK (2021). Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensembles in deep convolutional neural networks. arXiv e-prints, arXiv:2102.11519

  191. Wang Y, Choi EJ, Choi Y, Zhang H, Jin GY, Ko SB (2020) Breast cancer classification in automated breast ultrasound using multiview convolutional neural network with transfer learning. Ultrasound Med Biol 46(5):1119–1132

    Article  Google Scholar 

  192. Zhang H, Han L, Chen K, Peng Y, Lin J (2020) Diagnostic efficiency of the breast ultrasound computer-aided prediction model based on convolutional neural network in breast cancer. J Digit Imaging. https://doi.org/10.1007/s10278-020-00357-7

    Article  Google Scholar 

  193. Zhu YC, AlZoubi A, Jassim S, Jiang Q, Zhang Y, Wang YB, Ye XD, Du H (2020) A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics. https://doi.org/10.1016/j.ultras.2020.106300

    Article  Google Scholar 

  194. Shi J, Zhou S, Liu X, Zhang Q, Lu M, Wang T (2016) Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing 194:87–94

    Article  Google Scholar 

  195. Daoud MI, Abdel-Rehman S, Alazrai R (2019) Breast ultrasound image classification using a pre-trained convolutional neural network. In: Proceedings of 15th international conference on signal-image technology AND internet-based systems, Sorrento, Italy, pp. 167–171

  196. Chen DR, Hsiao YH (2008) Computer aided diagnosis in breast ultrasound. J Med Ultrasound 16(1):46–56

    Article  Google Scholar 

  197. Cheng HD, Shan J, Ju W, Guo Y, Zhang L (2010) Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recogn 43(1):299–317

    Article  MATH  Google Scholar 

  198. Jalalian A, Mashohor SBT, Mahmud HR, Saripan MIB, Ramli ARB, Karasfi B (2013) Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin Imaging 37(3):420–426

    Article  Google Scholar 

  199. Prabusankarlal KM, Thirumoorthy P, Manavalan R (2014) Computer aided breast cancer diagnosis techniques in ultrasound: a survey. Med Imaging Health Inform 4(3):331–349

    Article  Google Scholar 

  200. Shiji TP, Remya S, Lakshmanan R, Pratab T, Thomas V (2020) Evolutionary intelligence for breast lesion detection in ultrasound images: a wavelet modulus maxima and SVM based approach. J Intell Fuzzy Syst 38(5):6279–6290

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Dr. Shruti Thakur, Kamla Nehru Hospital, Shimla for explaining the different sonographic appearances exhibited by different breast tumors. The authors would also like to thank Director, Thapar Institute of Engineering and Technology, Patiala and Director, CSIR-CSIO, Chandigarh for constant patronage and support in carrying out the present research.

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  1. Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India

    Kriti & Ravinder Agarwal

  2. CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, 160030, India

    Jitendra Virmani

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Kriti, Virmani, J. & Agarwal, R. A Characterization Approach for the Review of CAD Systems Designed for Breast Tumor Classification Using B-Mode Ultrasound Images. Arch Computat Methods Eng 29, 1485–1523 (2022). https://doi.org/10.1007/s11831-021-09620-8

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