Abstract
Breast cancer is one of the widespread reasons of morbidity worldwide that begins in the cells of the tissues of morbidity worldwide in the woman community. Breast cancer can be confirmed by investigating the interior tissue regions in terms of Invasive- Ductal-Breast Carcinoma (IDC) and Invasive-lobular-Breast Carcinoma (ILC). Therefore, early diagnosis of breast tissue abnormalities is crucial to diminish the risk by enabling quick and efficient treatment. This research study aims to propose a comprehensive CAD system invasive ductal carcinoma (IDC) by employing the proposed deep learning-based algorithm using the histopathology images. The proposed scheme developed three different CNN models from scratch like ConvNet-A, ConvNet-B, and ConvNet-C by considering different layers that are 8, 9, and 19 layer, respectively. Furthermore, the performance has been validated against four popular machine learning models such as support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF) and logistic regression (LR). Experiments have been performed in two-steps; first, proposed CNN model is evaluated in different sample size, and achieved best accuracy of 88.7% and sensitivity of 92.6% with ConvNet-C with 100,000 sample images. Second, the best classification accuracy achieved by SVM if the number of images taken are more than 5000, because it has a regularization parameter which avoids over-fitting. It is clearly perceived that the proposed CNN algorithms lead to better classification accuracy for detection of IDC compared to state-of-art techniques and hence, this computational framework will act as a helping aid for the pathologist.
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Gupta, I., Nayak, S.R., Gupta, S. et al. A deep learning based approach to detect IDC in histopathology images. Multimed Tools Appl 81, 36309–36330 (2022). https://doi.org/10.1007/s11042-021-11853-5
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DOI: https://doi.org/10.1007/s11042-021-11853-5