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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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Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

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Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern.

Siti Salbiah Samsudin¹, Hamzah Arof¹, Sulaiman Wadi Harun¹

¹Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.

|September 20, 2022

Summary

This summary is machine-generated.

This study introduces an advanced image analysis technique for early skin cancer detection, achieving 98.9% accuracy in classifying seven types of skin lesions. The method effectively identifies precancerous skin lesions for timely intervention.

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Area of Science:

Dermatology and Medical Imaging
Computational Pathology
Artificial Intelligence in Healthcare

Background:

Skin cancer is a prevalent global health concern, with early detection of skin lesions being crucial for effective treatment.
Identifying precancerous skin lesions accurately and early can significantly improve patient outcomes and reduce mortality rates.

Purpose of the Study:

To develop and evaluate an image-based system for classifying seven different types of skin lesions.
To enhance the early identification of potentially cancerous skin lesions through automated analysis.

Main Methods:

Utilized Multi-Resolution Empirical Mode Decomposition (MREMD) to decompose skin lesion images into Bidimensional Intrinsic Mode Functions (BIMFs).
Employed active contour for region of interest (ROI) localization and Local Binary Pattern (LBP) for texture feature extraction (512 features).
Trained an Artificial Neural Network (ANN) classifier using 490 HAM10000 dataset images and validated with 315 test images.

Main Results:

The proposed method achieved a high overall classification accuracy of 98.9% for seven distinct skin lesion classes.
Feature extraction using MREMD and LBP effectively captured discriminative texture information from lesion ROIs.

Conclusions:

The developed image-based classification system demonstrates high efficacy in identifying various skin lesions.
This approach holds significant potential for aiding dermatologists in the early and accurate diagnosis of skin cancer.