DeepScan: A Deep Learning-Based Clinical Decision Support System for Breast Cancer Diagnosis Using DenseNet121-CBAM and LLM Integration

Abstract

Breast cancer diagnosis using ultrasound imaging remains challenging due to image noise, class imbalance, and limited interpretability of automated systems. This study presents DeepScan, a deep learning–based Clinical Decision Support System (CDSS) designed for research and educational use, which integrates a DenseNet121 backbone with a Convolutional Block Attention Module (CBAM) to enhance feature discrimination in breast ultrasound images. The system performs three-class classification (Normal, Benign, Malignant) using a structured preprocessing pipeline that includes resizing, ImageNet-based normalization, data augmentation, and controlled oversampling to address class imbalance. To improve transparency, Grad-CAM–based visual explanations are incorporated to highlight diagnostically relevant regions influencing model predictions. Beyond image-level classification, DeepScan integrates a Large Language Model (LLM)–based reasoning engine to generate BI-RADS–aligned, structured clinical reports and provide interactive explanations for users. Experimental evaluation on the BUSI dataset demonstrates strong discriminative performance, achieving AUC values of 0.998 for Normal tissue and 0.957 for both Benign and Malignant classes, with near real-time inference latency. The results indicate that combining attention-enhanced convolutional models with explainable AI and LLM-based reporting can improve both performance and interpretability, positioning DeepScan as a supportive CDSS framework for breast ultrasound analysis in pre-clinical and educational settings.