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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: Jul 30, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics.

Samy Bakheet^1,2, Shtwai Alsubai³, Aml El-Nagar¹

¹Faculty of Computers and Artificial Intelligence, Sohag University, Sohag 82524, Egypt.

Diagnostics (Basel, Switzerland)

|May 16, 2023

Summary

This study introduces a computer-aided diagnosis (CAD) framework for early malignant melanoma detection using dermoscopy images. The system achieved high accuracy, specificity, and sensitivity in classifying skin lesions.

Keywords:

computer-aided diagnosis cross-validation gentle adaboost histogram of oriented gradient k-nearest neighbors local binary patterns malignant melanoma skin cancer support vector machine

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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
Medical Imaging
Computer Science

Background:

Malignant melanoma is an aggressive skin cancer, emphasizing the need for early detection.
Computer-aided diagnosis (CAD) systems offer a promising approach for automated skin lesion analysis.

Purpose of the Study:

To develop an integrated CAD framework for rapid and accurate detection of malignant melanoma in dermoscopy images.
To classify melanocytic skin lesions as either melanoma or nevus.

Main Methods:

Image pre-processing using median and bottom-hat filtering for noise and artifact reduction.
Feature extraction via Histogram of Oriented Gradient (HOG) and Local Binary Patterns (LBP) descriptors.
Classification using Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Gentle AdaBoost (GAB) models.

Main Results:

The proposed CAD framework demonstrated competitive or superior performance compared to state-of-the-art methods.
Achieved high diagnostic metrics: 94% accuracy, 92% specificity, and 100% sensitivity on the MED-NODEE dataset.
10-fold cross-validation confirmed the framework's robustness.

Conclusions:

The integrated CAD framework provides an effective tool for the accurate diagnosis of melanoma from dermoscopy images.
Early detection and classification of skin lesions can be significantly improved with advanced computational methods.