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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: Sep 18, 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

Skin cancer detection using harmonic brown bear optimization enabled transfer learning.

Malathy Manickavasagam¹, Vaddadi Vasudha Rani², Uttam Kumar Giri³

¹Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai 600062, India.

Computational Biology and Chemistry

|June 21, 2025

Summary

This summary is machine-generated.

This study introduces a novel Convolutional Neural Network-Transfer Learning approach, enhanced by Harmonic Brown Bear Optimization (CNN-TL_Hr-BOA), for accurate skin cancer detection. The method significantly improves early diagnosis by effectively analyzing skin lesion images.

Keywords:

Medav filter Position and Context information Fusion attention Network Skin Cancer Detection Skin image Transfer Learning

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

Early skin cancer detection is crucial for successful treatment but challenging due to visual similarities between benign and malignant lesions.
Existing diagnostic methods often struggle with accuracy and robustness in identifying subtle differences in skin lesions.

Purpose of the Study:

To develop an advanced deep learning framework, CNN-TL_Hr-BOA, for enhanced accuracy and robustness in early-stage skin cancer detection.
To improve the identification of malignant skin lesions through sophisticated image processing and optimized model training.

Main Methods:

Image preprocessing included denoising with Medav filter and segmentation using PCF-Net.
Superpixel-mixing augmentation diversified training data, followed by feature extraction using RDWT.
A CNN model with DenseNet weights was optimized using Harmonic Analysis and Brown Bear Optimization (Hr-BOA) for hyperparameter tuning.

Main Results:

The CNN-TL_Hr-BOA model achieved high performance on the SIIM-ISIC Melanoma Classification dataset.
Key metrics included 91.754% accuracy, 93.755% True Positive Rate (TPR), and 89.766% True Negative Rate (TNR).

Conclusions:

The proposed CNN-TL_Hr-BOA framework demonstrates significant effectiveness in accurately detecting early-stage skin cancer.
This approach offers a robust solution for improving diagnostic accuracy in medical imaging for skin cancer identification.