MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms

• Published in: Computer methods and programs in biomedicine 2024-05, Vol.248, p.108121-108121, Article 108121
• Main Authors: Nissar, Iqra, Alam, Shahzad, Masood, Sarfaraz, Kashif, Mohammad
• Format: Article
• Language: English
• Subjects: Attention mechanism; Breast cancer; Deep learning; Molecular subtypes
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2024.108121

•Deep learning advancements: Deep learning transforms healthcare, excelling in imaging and cancer detection, including breast cancer.•Precision in diagnosis: Deep learning's accuracy detects subtle patterns and abnormalities often unnoticed by humans, elevating diagnosis.•Personalized treatment potential: Molecular subtyping in breast cancer tailors treatment strategies, improving outcomes and prognostic insights.•Innovative MOB-CBAM architecture: The MOB-CBAM model accurately predicts breast cancer types and molecular subtypes, enhancing personalized care.•Broad validation and impact: MOB-CBAM's performance extends to multiple datasets, hinting at its potential to revolutionize breast cancer diagnosis and treatment. Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recent medical studies highlight the significance of molecular subtype detection in breast cancer, aiding the development of personalized treatment plans as different subtypes of cancer respond better to different therapies. In this work, we propose a novel lightweight dual-channel attention-based deep learning model MOB-CBAM that utilizes the backbone of MobileNet-V3 architecture with a Convolutional Block Attention Module to make highly accurate and precise predictions about breast cancer. We used the CMMD mammogram dataset to evaluate the proposed model in our study. Nine distinct data subsets were created from the original dataset to perform coarse and fine-grained predictions, enabling it to identify masses, calcifications, benign, malignant tumors and molecular subtypes of cancer, including Luminal A, Luminal B, HER-2 Positive, and Triple Negative. The pipeline incorporates several image pre-processing techniques, including filtering, enhancement, and normalization, for enhancing the model's generalization ability. While identifying benign versus malignant tumors, i.e., coarse-grained classification, the MOB-CBAM model produced exceptional results with 99 % accuracy, precision, recall, and F1-score values of 0.99 and MCC of 0.98. In terms of fine-grained classification, the MOB-CBAM model has proven to be highly efficient in accurately identifying mass with (benign/malignant) and calcification with (benign/malignant) classification tasks with an impressive accuracy rate of 98 %. We have also cross-validated the efficiency