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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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Related Experiment Video

Updated: Dec 12, 2025

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

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

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Automatic skin lesion segmentation based on FC-DPN.

Pufang Shan¹, Yiding Wang¹, Chong Fu²

¹School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China.

Computers in Biology and Medicine

|August 10, 2020

Summary

This study introduces FC-DPN, a novel deep learning model for segmenting skin lesions in dermoscopy images. The method achieves high accuracy, outperforming existing algorithms on benchmark datasets.

Keywords:

Automatic skin lesion segmentation DPN DenseNets Dermoscopy FC-DenseNets ResNets

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Last Updated: Dec 12, 2025

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Area of Science:

Medical Image Analysis
Computer Vision
Dermatology

Background:

Automatic skin lesion segmentation is crucial for early diagnosis but faces challenges like image variability and artifacts.
Existing methods struggle with low contrast and diverse lesion characteristics.

Purpose of the Study:

To develop a novel and accurate automatic skin lesion segmentation method.
To improve feature representation and re-exploitation for enhanced segmentation performance.

Main Methods:

Proposed FC-DPN (Fully Convolutional Dual Path Network) integrating Fully Convolutional Networks (FCN) and Dual Path Networks (DPN).
Replaced dense blocks with sub-DPN projection and processing blocks for superior feature extraction.
Utilized revised ISBI 2017 Skin Lesion Challenge dataset and PH2 dataset for evaluation.

Main Results:

Achieved an 88.13% Dice coefficient and 80.02% Jaccard index on the modified ISBI 2017 dataset.
Obtained 90.26% Dice coefficient and 83.51% Jaccard index on the PH2 dataset.
Demonstrated superior performance compared to FC-DenseNets and other established segmentation algorithms.

Conclusions:

The proposed FC-DPN method offers a robust and accurate solution for skin lesion segmentation.
The novel architecture effectively captures discriminative features, addressing segmentation challenges in dermoscopy images.
FC-DPN shows significant potential for clinical applications in automated skin cancer screening.