Advancing Breast Cancer Diagnosis with Deep Layer-wise Feature Integration from Histopathology Images

Background

Breast Cancer (BC) remains a leading cause of cancer-related deaths among women. Diagnosis from histopathology images can be inconsistent in pathologist assessments, highlighting the need for reliable computer-aided diagnostic systems. Existing approaches overlook multi-layer features or employ extraction methods that lack generalizability.Objective: This study proposes a layer-wise feature integration framework to enhance BC histopathology classification.Material and Methods: In this analytical and computational study, using the BreakHis and ICIAR/BACH datasets, features are extracted from intermediate and high-level layers of pre-trained Convolutional Neural Networks (CNNs). Mutual Information (MI) between each layer’s features and true labels is computed to identify the most informative layers, and those with the highest MI are selected. To address overfitting, Principal Component Analysis (PCA) with optimal Cumulative Explained Variance (CEV) is applied. The transformed features are then concatenated and classified using a Support Vector Machine (SVM).Results: The method is evaluated using VGG19, DenseNet, and ConvNextTiny across both datasets and demonstrates significant improvements in image-wise and patient-wise classification. ConvNextTiny, enhanced with Synthetic Minority Oversampling Technique (SMOTE) for class-imbalance mitigation, achieves the highest accuracy of 99.14% at 40× in BreakHis dataset.