Multimodal Biomedical Image Segmentation by Using Multi-Path U-Net

Abstract

Early detection of skin lesions is essential for the success of treatment depending on the earliest possible detection of skin cancer lesions. Segmentation of skin cancer lesions is one of the most important early steps. In this regard, classic U-Net which is based on deep neural networks is the most popular architecture for medical image segmentation. However, the classic U-Net architecture lacks certain aspects. In this approach, we propose a lightweight model designed to minimize memory usage in the deeper network layers and to reduce training and testing time. We achieve this by leveraging Multi-Level Blocks, which exclusively utilize 3x3 convolution operations. Additionally, we have utilized multiple convolutions to facilitate the transfer of information from the encoding to the decoding stage. This approach aims to minimize the semantic gap between the two stages. We have termed this information transfer path the encoder-decoder path. Our method has demonstrated outstanding performance in key metrics when tested on the PH2 dataset and has shown superior performance in terms of Accuracy and Jaccard Index on the ISIC-2017 dataset compared to the latest methods reported in existing publications. The Multi-Path U-Net method effectively recognizes and precisely segments complex features such as weak boundaries, shape, and color irregularities, and multi-part lesions with diverse color intensities.