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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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Computer-Aided Diagnosis Algorithm for Classification of Malignant Melanoma Using Deep Neural Networks.

Chan-Il Kim¹, Seok-Min Hwang¹, Eun-Bin Park¹

¹Department of Biomedical Engineering, Keimyung University, Daegu 42601, Korea.

Sensors (Basel, Switzerland)

|August 28, 2021

Summary

This study introduces an AI algorithm for detecting malignant melanoma from skin images. The deep learning model accurately classifies skin lesions, aiding in early cancer detection.

Keywords:

computer aided diagnosis convolutional neural network malanoma

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Malignant melanoma is a significant cause of cancer death, often challenging to diagnose from images.
Early detection of melanoma is crucial for improving patient outcomes.

Purpose of the Study:

To develop and evaluate a deep learning-based computer-aided diagnostic (CAD) algorithm for classifying malignant melanoma and benign skin tumors.
To improve the accuracy and efficiency of melanoma diagnosis using AI.

Main Methods:

A deep learning model combining tumor lesion segmentation (U-Net) and classification (Convolutional Neural Networks) was developed.
The model was trained and validated using RGB skin images and expert labeling data.
Performance was evaluated using metrics such as the Dice Similarity Coefficient and classification accuracy.

Main Results:

The U-Net segmentation model achieved an 81.1% Dice Similarity Coefficient against expert labels.
The classification model demonstrated an 80.06% accuracy in identifying malignant melanoma.
The proposed AI algorithm shows promise for clinical application.

Conclusions:

The developed AI algorithm shows significant potential as a computer-aided diagnostic tool for early malignant melanoma detection.
This technology can assist healthcare professionals in differentiating between malignant and benign skin lesions.
Further research and validation are warranted to integrate this AI tool into clinical practice for melanoma screening.