Advanced Medical Image Segmentation with Multi-Scale Feature Fusion and Attention Mechanisms

Abstract

Skin diseases and lung inflammation present significant diagnostic challenges, necessitating precise and efficient methods to aid clinicians. The early detection of pulmonary nodules is particularly crucial, yet traditional diagnostic methods often lead to radiologist fatigue and increased error rates. Deep learning has emerged as a promising tool to enhance clinical diagnostics, but conventional techniques struggle with the accurate segmentation of small and irregularly shaped lesions, leading to reduced accuracy. This paper proposes a novel lesion image segmentation method based on the UNeXt architecture, integrating multi-scale feature fusion and attention mechanisms. The multi-scale feature fusion ensures comprehensive feature extraction, while the attention mechanism enhances the focus on critical regions, improving edge feature sensitivity. Additionally, a joint loss function is employed to optimize the model's performance. The proposed method aims to significantly improve the segmentation accuracy of pulmonary nodules, providing a robust tool for early diagnosis and treatment, thereby demonstrating the transformative potential of deep learning in clinical diagnostics.