Abstract
One of the foremost decease causes in the world is the Liver Cancer. Using practical radiology the liver lesions can be determined with accurate dramatization, due to the hurt caused by wound or disease, those ranges of tissues are damaged the body are liver lesions Those anomalous tissues which are found in the liver are referred as liver lesions. These damaged regions having different intensities of pixel can be recognized by differentiating it from other regions, in the CT scan. The most prohibitive, difficult and time-consuming task is physical cleavage of this CT scan in proper clinical treatment. On the other hand, automatic segmentation is identical challenging task, due to several factors, including liver stretch over 150 slices in a CT image, having small ferocity conflict between lesions and other nearby similar tissues and indefinite shape of the lesions is to detected An important prerequisite task before any surgical intervention is liver tumors segmentation. This paper reviews a variety of liver tumor detection algorithms and methodologies used for liver tumor analysis. The proposed deep learning approach such as Probabilistic neural network is proposed to detect the liver tumor and diagnose with the experimental results and it is compared with different methodologies.
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04 July 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04271-4
References
Bharathi A, Natarajan AM (2011) Effective grouping of malignant growth utilizing bolster vector machines and adjusted extraordinary learning machine dependent on investigation of fluctuation highlights. Am J Appl Sci 8(12):1295
Chartrand G et al (2017) Liver division on CT and MR utilizing Laplacian work enhancement. IEEE Trans Biomed Eng 64(9):2110–2121
Chlebus G et al (2017) Neural system based programmed liver tumor division with irregular woods based up-and-comer separating. arXiv preprint arXiv:1706.00842
Coe JV et al (2014) Imaging infrared spectroscopy for obsession free liver tumor discovery. Imaging, manipulation, and analysis of biomolecules, cells, and tissues XII. Vol. 8947. International Society for Optics and Photonics
Davis R et al (2017) An efficient automatic liver tumor detection from CT images. Imperial J Interdiscipl Res 3.3
Huang W et al (2012) A self-loader way to deal with the division of liver parenchyma from 3D CT pictures with outrageous learning machine. Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. IEEE
Huang W et al (2013) Liver tumor recognition and division utilizing bit based outrageous learning machine. Engineering in prescription and science society (EMBC), 2013 35th yearly worldwide meeting of the IEEE, IEEE
Huang W et al (2014) Irregular component subspace troupe based extraordinary learning machine for liver tumor location and division. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, IEEE
Gibson E et al (2017) Profound remaining systems for programmed division of laparoscopic recordings of the liver. Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling. Vol. 10135. International Society for Optics and Photonics
Han B, Han Y, Gao X et al (2019) Boundary constraint factor embedded localizing active contour model for medical image segmentation. J Ambient Intell Human Comput 10:3853–3862
Hemalatha V, Sundar C (2020) Automatic liver cancer detection in abdominal liver images using soft optimization techniques. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01885-4
Li W, Fucang J, Qingmao H (2015) Programmed division of liver tumor in CT pictures with profound convolutional neural systems. J Comput Commun 3(11):146
Raj A, Jayasree M (2016) Computerized liver tumor detection using Markov random field segmentation. Procedia Technol 24:1305–1310
Schnabel JA, Arridge SR (1995) Dynamic contour models for shape description utilizing multiscale differential invariants. BMVC 1:197–206
Schnabel JA, Arridge SR (1996) Multiscale shape depiction of MR cerebrum pictures utilizing dynamic form models. Medical Imaging 1996: Image Processing. Vol. 2710. International Society for Optics and Photonics
Shaukat F, Raja G, Ashraf R, Khalid S, Ahmad M, Ali A (2019) Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features. J Ambient Intell Hum Comput 10(10):4135–4149
Vivanti R et al (2015) Programmed liver tumor division in follow up CT studies utilizing convolutional neural systems. Proceedings Fix Based Methods in Medical Image Processing Workshop. Vol. 2
Yue H et al (2016) A Markov random field model for image segmentation of rice planthopper in rice fields. J Eng Sci Technol Rev 9(2):31–38
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04271-4"
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Sureshkumar, V., Chandrasekar, V., Venkatesan, R. et al. RETRACTED ARTICLE: Improved performance accuracy in detecting tumor in liver using deep learning techniques. J Ambient Intell Human Comput 12, 5763–5770 (2021). https://doi.org/10.1007/s12652-020-02107-7
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DOI: https://doi.org/10.1007/s12652-020-02107-7