Explainable Multi-Module Semantic Guided Attention Network for Accurate Medical Image Segmentation - R. Inbaraj; V. Pavithra; R. Vinitha; T.S. Reshmi (International Journal of Computational and Experimental Science and Engineering, 2025)
Accurate medical image segmentation is of utmost importance in a wide range of clinical applications, playing a vital role in disease diagnosis and treatment planning. This research presents the application of the Explainable Multi-Module Semantic Guided Attention Network (EM-SGAN) with the optimization technique of unbounded variance Adaptive Moment Estimation (AMSGrad) for breast cancer image segmentation. EM-SGAN is a deep learning model that integrates multiple modules to enhance the accuracy and interpretability of the segmentation process. The key components of EM-SGAN include an encoder-decoder framework, attention mechanism, semantic guidance module, and explainability module. By incorporating the AMSGrad optimizer, which addresses the unboundedness issue of the second-moment estimate, EM-SGAN achieves stable convergence and improved optimization. Experimental evaluations on breast cancer image segmentation tasks demonstrate the effectiveness of EM-SGAN with unbounded variance AMSGrad in accurately segmenting cancerous regions. The proposed approach significantly advances the field of medical image segmentation by offering a dependable and understandable solution for breast cancer analysis.