Explainable AI for Breast Cancer Diagnosis: A Convolutional Autoencoder with Modified Loss Functions and Grad-CAM Visualization for Histopathology Image Classification

The study proposes a novel deep learning model, a Convolutional Autoencoder with modified loss functions (CAE-MLS) for explainable breast cancer diagnosis through histopathology image analysis. To enhance model interpretability, we used Gradient-weighted Class Activation Mapping (GradCAM) visualization, which emphasizes areas of interest that affect the model's diagnostic decisions. The optimization of the model is achieved by using a custom loss function which combines mean absolute error, mean squared error, and structural similarity index to balance pixel-level accuracy with quality in image reconstruction. Our model achieves 91\% classification accuracy, with 93% precision for Malignant tumour detection. The proposed model attained a best-balanced performance for both classes, with F1-scores of 0.88 and 0.92 for benign and malignant cases in the 10x magnifications, respectively. Grad-CAM visualizations confirm the model's ability to focus on clinically relevant image regions at both 10x and 40x magnifications, providing healthcare professionals with transparent insights into the decision-making process.