Application of Convolutional Neural Networks in Breast Cancer Detection: Hybrid and Attention-based Models

Abstract

For breast cancer, a malignant tumor developing from breast epithelial tissue, the limitations of traditional diagnostic methods (e.g., diagnostic errors and invasiveness) create critical challenges, underscoring the urgent need for artificial intelligence-assisted technological development. This paper systematically reviews the applications of Convolutional Neural Networks (CNNs) across the entire workflow of breast cancer, including screening, diagnosis, and prognosis, with a focus on hybrid CNNs such as architectures combining Transformer/Long Short-Term Memory (LSTM) and CNN-Support Vector Machine (SVM) models and attention-based CNNs such as contour-enhanced attention and cross-attention mechanisms. It analyzes how these models automate feature extraction from medical images to achieve breast lesion detection, benign-malignant differentiation, and prognosis prediction. Results show that hybrid models like Fusion of Hybrid Deep Features (FHDF) achieve over 98% classification accuracy on datasets such as MIAS by fusing features from multiple CNNs, while attention-based models like Convolutional Block Attention Module (CBAM)-Xception attain an Area Under Curve (AUC) of 0.97 and an accuracy of 89.1% in differentiating benign and malignant lesions. However, challenges remain, including insufficient interpretability, cross-institutional data heterogeneity, and privacy risks. The study proposes integrating medical expert systems and applying transfer learning and domain adaptation techniques to enhance model reliability and generalizability, promoting the translation of CNN technologies into clinical practice and constructing a precise and trustworthy AI-driven breast cancer diagnosis and treatment system.