AOA-guided hyperparameter refinement for precise medical image segmentation
Hossam Magdy Balaha, Waleed M. Bahgat, Mansourah Aljohani, Amna Bamaqa, El-Sayed Atlam, Mahmoud Badawy, Mostafa A. Elhosseini · Alexandria Engineering Journal · 2025
Medical image segmentation faces significant challenges, including the need for extensive annotated data, the impact of hyperparameters, and the limitations of traditional CNN models. Breast cancer (BC) and COVID-19 imaging, in particular, require precise segmentation for accurate diagnosis and treatment planning. This study introduces a novel framework that utilizes the Archimedes Optimization Algorithm (AOA) to optimize hyperparameters for medical image segmentation, aiming to enhance accuracy and efficiency. We propose a four-stage framework for hyperparameter optimization using AOA. The framework consists of: (1) Population Initialization, (2) Fitness Function Evaluation, (3) Population Updating, and (4) Results Logging. The framework optimizes key hyperparameters, including activation functions, loss functions, optimizers, and batch sizes, using a hybrid loss function combining Focal Tversky and IoU. The proposed framework was rigorously evaluated on two medical datasets: the BUSI dataset for BC and the COVID-19 CT scan lesion segmentation dataset. The R2 U-Net 2D model achieved an accuracy of 95.7% on the BUSI dataset, while the V-Net model achieved 99.2% accuracy on the COVID-19 dataset. The AOA-guided framework demonstrated superior performance compared to existing methods, with Dice coefficients of 0.675 and 0.723 for the BUSI and COVID-19 datasets, respectively. Convergence curves and performance metrics validated the stability and efficiency of AOA in optimizing hyperparameters. The AOA-guided framework significantly improves medical image segmentation by automating hyperparameter optimization. The results highlight the potential of AOA as a powerful tool for enhancing segmentation accuracy and robustness. • Using Archimedes Optimization Algorithm (AOA) for hyperparameter tuning in medical image segmentation. • Integrating AOA with V-Net, U-Net 2D, and R2 U-Net 2D to enhance segmentation accuracy. • Evaluating the performance on the BUSI dataset (breast cancer) and the COVID-19 CT scan lesion dataset. • Comparative analysis shows AOA outperforms existing methods in segmentation accuracy.