Hybrid Deep Learning Architectures for Multimodal AI-Driven Breast Cancer Detection: A Comparative Analysis of Computational Efficiency and Diagnostic Accuracy

Disease diagnosis is a complex challenge in medicine, with artificial intelligence (AI), machine learning, and deep learning offering innovative solutions to enhance evidence-based decision-making. This paper focuses on AIdriven diagnostic and treatment strategies for breast cancer, emphasizing the combination of multimodal medical data, including imaging, text, genetic data, and physiological signals.. These methods improve diagnosis and clinical outcomes by utilizing public datasets, feature engineering, and advanced classification models. Traditional breast cancer diagnostics often rely on unimodal approaches, but multimodal techniques now incorporate histopathology images, genomics, clinical notes, and patient history, significantly enhancing diagnostic accuracy. This review explores applications such as Visual Question Answering (VQA) and semantic segmentation. Explainable AI (XAI) tools, including Grad-CAM, SHAP, and LIME, improve interpretability, foster clinician trust, and encourage patient engagement. Hybrid deep learning models combining mammography, ultrasound, and histopathology minimize false positives and boost detection accuracy. Feature fusion and optimized transfer learning make these systems viable in resource-constrained settings. Although challenges such as limited datasets and interpretability persist, hybrid deep learning systems promise significant advancements, offering computational efficiency and improved patient outcomes.