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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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Updated: Dec 21, 2025

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

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DSNet: Automatic dermoscopic skin lesion segmentation.

Md Kamrul Hasan¹, Lavsen Dahal¹, Prasad N Samarakoon²

¹Computer Vision and Robotics Institute, University of Girona, Spain.

Computers in Biology and Medicine

|May 19, 2020

Summary

This summary is machine-generated.

A new deep learning network, Dermoscopic Skin Network (DSNet), offers robust automatic skin lesion segmentation for improved melanoma detection. DSNet achieves superior performance on public datasets, outperforming existing methods.

Keywords:

Computer-aided Diagnosis (CAD)Deep learning Melanoma detection Skin lesion segmentation

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Published on: August 18, 2022

Area of Science:

Medical Image Analysis
Computer-Aided Diagnosis
Dermatology

Background:

Automatic skin lesion segmentation is vital for melanoma detection in Computer-aided Diagnosis (CAD).
Challenges include lesion variability in color, texture, shape, and indistinct boundaries.
Accurate segmentation is crucial for reliable CAD system performance.

Purpose of the Study:

To introduce a novel, automatic semantic segmentation network, Dermoscopic Skin Network (DSNet), for robust skin lesion segmentation.
To enhance network efficiency by employing depth-wise separable convolutions.
To compare DSNet's performance against U-Net and Fully Convolutional Network (FCN8s) architectures.

Main Methods:

Developed DSNet, a semantic segmentation network utilizing depth-wise separable convolutions.
Implemented U-Net and FCN8s for comparative analysis.
Evaluated models on the ISIC-2017 and PH2 dermoscopic image datasets.

Main Results:

DSNet achieved a mean Intersection over Union (mIoU) of 77.5% on ISIC-2017 and 87.0% on PH2.
DSNet outperformed the ISIC-2017 challenge winner by 1.0% mIoU.
DSNet surpassed U-Net (3.6% mIoU) and FCN8s (6.8% mIoU) on the ISIC-2017 dataset.

Conclusions:

DSNet demonstrates superior performance in skin lesion segmentation compared to other methods.
The network generates improved segmentation masks, potentially enhancing melanoma detection accuracy.
The trained model, source code, and predicted masks are publicly available.