Papers › MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation

MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation

27 Mar 2022arXiv:2203.14341archive 2025-07-28

Hritam Basak, Rohit Kundu, Ram Sarkar

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: PH², 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

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Code

hritam-98/MFSNet official report

rohit-kundu/mfsnet mentioned in papermentioned on GitHub report

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Tasks

Decoder Lesion Segmentation Medical Image Analysis Segmentation Semantic Segmentation Skin Lesion Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Semantic Segmentation	HAM10000	MFSNet	Average Dice	90.6	#1 of 1	Archive leaderboard	report
Semantic Segmentation	HAM10000	MFSNet	Average IOU	90.2	#1 of 1	Archive leaderboard	report
Semantic Segmentation	ISIC 2017	MFSNet	Average Dice	98.7	#1 of 1	Archive leaderboard	report
Semantic Segmentation	PH2	MFSNet	Average Dice	95.4	#2 of 2	Archive leaderboard	report
Semantic Segmentation	PH2	MFSNet	Average IOU	0.914	#2 of 2	Archive leaderboard	report
Skin Lesion Segmentation	ISIC 2017	MFSNet	Mean IoU	97.4	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Convolution Global Average Pooling Kaiming Initialization ReLU Res2Net Res2Net Block Residual Connection

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