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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: Jun 21, 2025

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

An Intelligent Mechanism to Detect Multi-Factor Skin Cancer.

Abdullah¹, Ansar Siddique¹, Kamran Shaukat^2,3

¹Department of Computer Sciences, Bahria University Lahore Campus, Lahore 54600, Punjab, Pakistan.

Diagnostics (Basel, Switzerland)

|July 13, 2024

Summary

This summary is machine-generated.

This study introduces a deep sequential convolutional neural network (CNN) model for accurate skin lesion classification. The model achieved 96.25% accuracy, outperforming existing methods for improved melanoma detection.

Keywords:

convolutional neural networks deep learning intelligent tool machine learning melanoma skin lesions

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Published on: August 3, 2011

Area of Science:

Artificial Intelligence
Medical Imaging
Dermatology

Background:

Deep learning, particularly convolutional neural networks (CNNs), represents a state-of-the-art approach in computer-aided medical diagnosis.
Accurate classification of skin lesions is crucial for timely and effective patient treatment.

Purpose of the Study:

To develop and evaluate a deep sequential CNN model for enhanced skin lesion classification.
To improve the accuracy and efficiency of detecting malignant and benign skin lesions.

Main Methods:

A two-stage deep sequential CNN model was proposed for image preprocessing, feature extraction, and lesion detection.
The model was trained, validated, and tested on the HAM 10,000 dataset.
A web tool was integrated for enhanced visualization and patient health diagnosis support.

Main Results:

The sequential CNN model achieved an accuracy of 96.25% in classifying skin lesions.
The proposed model demonstrated superior performance compared to various existing methods, including CNN transfer learning, VGG 19, ResNet-50 + VGG-16, Inception v3, Vision Transformers, and Entropy-NDOELM.
Evaluation methods and user feedback validated substantial improvements over current state-of-the-art techniques.

Conclusions:

Deep learning, CNNs, and sequential CNNs show significant potential for disease detection and classification in medical imaging.
The developed model offers a promising advancement for revolutionizing melanoma detection and enhancing patient care.