MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation

TL;DR

MFSNet is a deep learning framework that uses multi-scale features and attention modules for accurate skin lesion segmentation, outperforming existing methods on multiple datasets.

Contribution

The paper introduces MFSNet, a novel multi-focus segmentation network utilizing Res2Net backbone and attention modules for improved skin lesion segmentation.

Findings

Outperforms state-of-the-art methods on three datasets

Uses multi-scale feature maps for better segmentation accuracy

Employs boundary and reverse attention modules for refined results

Abstract

Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis. Skin cancer is one of the most common types of cancer globally, and its early diagnosis is pivotal for the complete elimination of malignant tumors from the body. This research develops an Artificial Intelligence (AI) framework for supervised skin lesion segmentation employing the deep learning approach. The proposed framework, called MFSNet (Multi-Focus Segmentation Network), uses differently scaled feature maps for computing the final segmentation mask using raw input RGB images of skin lesions. In doing so, initially, the images are preprocessed to remove unwanted artifacts and noises. The MFSNet employs the Res2Net backbone, a recently proposed convolutional neural network (CNN), for obtaining deep features used in a Parallel Partial Decoder (PPD) module to get a global map of the segmentation mask. In different stages of the network, convolution features and multi-scale maps are used in two boundary attention (BA) modules and two reverse attention (RA) modules to generate the final segmentation output. MFSNet, when evaluated on three publicly available datasets: $PH^2$, ISIC 2017, and HAM10000, outperforms state-of-the-art methods, justifying the reliability of the framework. The relevant codes for the proposed approach are accessible at https://github.com/Rohit-Kundu/MFSNet