Bilevel Hyperparameter Optimization and Neural Architecture Search for Enhanced Breast Cancer Detection in Smart Hospitals Interconnected With Decentralized Federated Learning Environment

• Published in: IEEE access Vol. 12; pp. 63618 - 63628
• Main Authors: Khan, Salabat, Nosheen, Fariha, Naqvi, Syed Shehryar Ali, Jamil, Harun, Faseeh, Muhammad, Ali Khan, Murad, Kim, Do-Hyeun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Ant colony optimization; Breast cancer; breast cancer detection; Cancer; Collaboration; Cooperative learning; Data models; decentralized federated learning; Detection algorithms; Federated learning; Hospitals; Hyperparameter optimization; Intelligent networks; Machine learning; Mammography; Medical imaging; Medical research; metaheuristic ACO; Metaheuristics; neural architecture search; Optimization; Public health; School environment; Solid modeling; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3392572

Breast cancer, a widespread malignancy predominantly affecting women aged 40 and above, presents a significant global health challenge with high mortality rates. The scarcity of medical data underscores the need for collaborative efforts among hospitals to enhance automated breast cancer detection. This research employs decentralized Federated Learning (FL) to facilitate cooperative learning across an interconnected smart hospital network, addressing data privacy, regulatory compliance, voluminous medical image data, and the necessity for distributed machine learning. Our innovative approach integrates Ant Colony Optimization (ACO) for hyperparameter fine-tuning and Neural Architecture Search (NAS) in a collaborative framework for smart hospitals linked with decentralized edge intelligent networks. This optimization strategy significantly improves the performance of our breast cancer detection system. Through a comprehensive experimental study (including diverse datasets), we classify Normal vs. Mass and Benign vs. Malignant regions in mammograms within a decentralized, federated collaborative learning environment. Empirical results consistently highlight the superiority of models trained using our method over individual hospital client-level training. Our method yielded significant improvements across evaluation measures: for Normal vs. Mass, achieving 92.6% sensitivity, 93.0% specificity, and 93.0% accuracy; for Benign vs. Malignant, achieving 89.6% sensitivity, 91.6% specificity, and 89.7% accuracy. Moreover, it has obtained a 6% and 5% increase in accuracy for Normal vs. Mass and Benign vs. Malignant cases, respectively, compared to the PSO-based HPO method. This evidence underscores the potential of collaborative approaches, emphasizing decentralized FL as a robust paradigm in medical research. The incorporation of ACO optimization reinforces the effectiveness of the proposed computer-aided diagnosis (CAD) system, marking a noteworthy advancement in the ongoing fight against breast cancer.