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