An Optimal Deep Learning Approach to BCa Tissue Detection using Case Studies
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Abstract
In this paper, we present a Deep Learning (DL) model with optimized performance for breast cancer (BCa) tissue classication. A simple DL approach is applied to the analysis of invasive ductal carcinoma (IDC) tissue, which is the most common BCa subtype. Binary classification of non-IDC and IDC tissues is proposed using Convolutional Neural Networks (CNN) in the training and prediction phases. Our trained model achieved F1 and sensitivity scores of 0.88, as well as micro-average values of 0.94 for the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and 0.95 for the area under the precision-recall curve. Since the le size of our model is small, it has the potential for application in real-world scenarios.
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