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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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BLA-Net:Boundary learning assisted network for skin lesion segmentation.

Ruiqi Feng¹, Li Zhuo¹, Xiaoguang Li¹

¹Faculty of Information Technology, Beijing University of Technology, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, China.

Computer Methods and Programs in Biomedicine

|October 26, 2022

Summary

A new boundary learning assisted network (BLA-Net) improves skin lesion segmentation accuracy. This method enhances boundary detection for computer-aided diagnosis, achieving state-of-the-art results.

Keywords:

Auxiliary boundary learning network Dynamic deformable convolution Global context information extraction module Skin lesion segmentation

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Area of Science:

Medical Image Analysis
Computer Vision
Artificial Intelligence

Background:

Automatic skin lesion segmentation is crucial for computer-aided diagnosis.
Existing segmentation networks struggle with accurate boundary detection of skin lesions.

Purpose of the Study:

To propose a novel boundary learning assisted network (BLA-Net) for improved skin lesion segmentation.
To enhance the accuracy of detecting skin lesion boundaries.

Main Methods:

Developed BLA-Net using ResNet34 backbone in an encoder-decoder framework.
Incorporated Dynamic Deformable Convolution and a Global Context Information Extraction Module in the Primary Segmentation Network (PSNet).
Utilized an Auxiliary Boundary Learning Network (ABLNet) with an object regional attention mechanism for precise boundary detection.

Main Results:

Achieved Jaccard Index of 86.6% and 84.8% on ISBI 2016 and ISIC 2018 datasets.
Obtained Dice Coefficients of 92.4% and 91.2% on ISBI 2016 and ISIC 2018 datasets.
Demonstrated superior segmentation performance on public dermoscopy datasets.

Conclusions:

BLA-Net achieves state-of-the-art segmentation accuracy compared to existing methods.
The proposed method requires fewer model parameters.
BLA-Net can significantly assist dermatologists in clinical diagnosis and treatment.