Background and objective

Breast cancer, the most intrusive form of cancer affecting women globally. Next to lung cancer, breast cancer is the one that provides a greater number of cancer deaths among women. In recent times, several intelligent methodologies were come into existence for building an effective detection and classification of such noxious type of cancer. For further improving the rate of early diagnosis and for increasing the life span of victims, optimistic light of research is essential in breast cancer classification. Accordingly, a new customized method of integrating the concept of deep learning with the extreme learning machine (ELM), which is optimized using a simple crow-search algorithm (ICS-ELM). Thus, to enhance the state-of-the-art workings, an improved deep feature-based crow-search optimized extreme learning machine is proposed for addressing the health-care problem. The paper pours a light-of-research on detecting the input mammograms as either normal or abnormal. Subsequently, it focuses on further classifying the type of abnormal severities i.e., benign type or malignant.

Materials and methods

The digital mammograms for this work are taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mammographic Image Analysis Society (MIAS), and INbreast datasets. Herein, the work employs 570 digital mammograms (250 normal, 200 benign and 120 malignant cases) from CBIS-DDSM dataset, 322 digital mammograms (207 normal, 64 benign and 51 malignant cases) from MIAS database and 179 full-field digital mammograms (66 normal, 56 benign and 57 malignant cases) from INbreast dataset for its evaluation. The work utilizes ResNet-18 based deep extracted features with proposed Improved Crow-Search Optimized Extreme Learning Machine (ICS-ELM) algorithm.

Results

The proposed work is finally compared with the existing Support Vector Machines (RBF kernel), ELM, particle swarm optimization (PSO) optimized ELM, and crow-search optimized ELM, where the maximum overall classification accuracy is obtained for the proposed method with 97.193% for DDSM, 98.137% for MIAS and 98.266% for INbreast datasets, respectively.

Conclusion

The obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the automatic detection and classification of breast cancer.

中文翻译：

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使用改进的深度学习极限学习机自动检测和分类乳房 X 线照片

背景和目标

乳腺癌是影响全球女性的最具侵入性的癌症。除了肺癌，乳腺癌是导致女性癌症死亡人数最多的癌症。最近，出现了几种智能方法来建立对这种有害癌症类型的有效检测和分类。为了进一步提高早期诊断率和延长受害者的寿命，乐观的研究光线对于乳腺癌的分类至关重要。因此，一种新的定制方法将深度学习的概念与极限学习机 (ELM) 相结合，该方法使用简单的群体搜索算法 (ICS-ELM) 进行了优化。因此，为了提高最先进的工作，提出了一种改进的基于深度特征的群体搜索优化的极限学习机来解决医疗保健问题。该论文对检测输入的乳房 X 线照片是否正常或异常进行了研究。随后，重点进一步分类异常严重程度的类型，即良性或恶性。

材料和方法

这项工作的数字乳房 X 线照片取自 DDSM (CBIS-DDSM)、乳房 X 线图像分析协会 (MIAS) 和 INbreast 数据集的策划乳房成像子集。在这里，这项工作使用了来自 CBIS-DDSM 数据集的 570 个数字乳房 X 线照片（250 个正常、200 个良性和 120 个恶性病例）、来自 MIAS 数据库的 322 个数字乳房 X 线照片（207 个正常、64 个良性和 51 个恶性病例）和 179 个全场数字乳房 X 线照片（来自 INbreast 数据集的 66 个正常病例、56 个良性病例和 57 个恶性病例）用于评估。该工作利用基于 ResNet-18 的深度提取特征和改进的 Crow-Search 优化极限学习机 (ICS-ELM) 算法。

结果

最后将所提出的工作与现有的支持向量机（RBF 内核）、ELM、粒子群优化（PSO）优化的 ELM 和群体搜索优化的 ELM 进行比较，其中所提出的方法获得了最大的整体分类准确率，为 97.193%对于 DDSM，MIAS 和 INbreast 数据集分别为 98.137% 和 98.266%。

结论

获得的结果表明，所提出的计算机辅助诊断 (CAD) 工具对于乳腺癌的自动检测和分类是稳健的。