Explainable and Efficient AI-Based System for Automated Breast Cancer Diagnosis Using EfficientNet-B3, CBAM, and Grad-CAM

Abstract

Breast cancer is one of the leading causes of cancerrelated deaths among women worldwide, making early and accurate diagnosis essential for improving patient survival and treatment outcomes. Traditional histopathological examination is considered the gold standard for breast cancer diagnosis; however, manual analysis is time-consuming, labor-intensive, and subject to inter-observer variability. To address these challenges, this paper presents an Explainable and Efficient AI-Based System for Automated Breast Cancer Diagnosis using deep learning. The proposed methodology employs the BreakHis histopathological image dataset, where images are preprocessed through resizing and normalization before being analyzed using a fine-tuned EfficientNet-B3 model integrated with a Convolutional Block Attention Module (CBAM) to enhance feature representation. Gradient-weighted Class Activation Mapping (Grad-CAM) is incorporated to provide visual explanations of the model’s predictions, improving transparency and interpretability. The trained model achieved a classification accuracy of 97.47% on the test dataset, demonstrating high performance in distinguishing benign and malignant breast cancer images. Furthermore, a Streamlitbased web application enables real-time image upload, prediction, confidence estimation, and explainability visualization. The proposed system offers an efficient, accurate, and interpretable decision-support tool that has the potential to assist pathologists in clinical breast cancer diagnosis while promoting trust in AIdriven healthcare applications.