EfficientSkinSegNet: A Lightweight ConvolutionalNeural Network for Accurate Skin LesionSegmentation

Abstract Accurate segmentation of skin lesions is crucial for the early detection and treatment of skin cancer. In this study, we propose EfficientSkinSegNet, a novel lightweight convolutional neural network architecture specifically designed for precise skin lesion segmentation. EfficientSkinSegNet incorporates efficient feature extraction encoders and decoders, leveraging multi-head convolutional attention and spatial channel attention mechanisms to extract and enhance informative features while eliminating redundant ones. Furthermore, a multi-scale feature fusion module is introduced in the skip connections to facilitate effective fusion of features at different scales. Experimental evaluations on benchmark datasets demonstrate that EfficientSkinSegNet outperforms state-of-the-art methods in terms of segmentation accuracy while maintaining a compact model size. The proposed network shows promise for practical clinical diagnostic applications, providing a balance between segmentation performance and computational efficiency. Future research will focus on evaluating EfficientSkinSegNet’s performance on diverse semantic segmentation tasks and optimizing it for medical image analysis.