Multi-Scale Attention FocusNet: A Precise Image Segmentation and Classification Approach for Advancing Liver Lesion Analysis

Abstract

In the realm of medical imaging, the accurate segmentation of liver lesions plays a crucial role in diagnosis and treatment planning. Despite promising results, traditional segmentation approach for liver cancer analysis may encounter challenges in accurately delineating lesions due to its reliance on fixed receptive fields, potentially leading to suboptimal performance in capturing complex variations in lesion and surrounding tissue characteristics. This research introduces the Multi-Scale Attention FocusNet (MA-FNet), a novel methodology intended to improve the precision of liver lesion segmentation. Leveraging advanced techniques in image analysis and attention mechanisms, this approach addresses the challenges posed by varying lesion sizes and complex backgrounds. Through a comprehensive evaluation on publicly available datasets and comparison with state-of-the-art methods, the efficacy and robustness of the proposed approach are demonstrated. The proposed work experimental outcome indicates significant advancements in liver lesion analysis, promising improved clinical decision-making and patient outcomes.