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

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An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector

Qianqian Liu¹, Hiroto Kawashima¹, Asad Rezaei Sofla^2,3

¹Laboratory of Microbiology and Immunology, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba 260-8675, Japan.

|October 27, 2023

Summary

A new method uses reinforcement learning and the Enhanced Fish Migration Optimizer (EFMO) algorithm for optimal melanoma detection. This approach enhances early skin cancer diagnosis, improving patient outcomes.

Keywords:

Enhanced fish migration optimizer (EFMO)Feature extraction Feature selection Melanoma detection Reinforcement learning Support vector machine

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Area of Science:

Dermatology and Artificial Intelligence
Computational Biology and Machine Learning

Background:

Early detection of melanoma is critical for reducing mortality from this aggressive skin cancer.
Current diagnostic methods include invasive biopsies and non-invasive intelligent approaches.
There is a need for improved, efficient, and accurate methods for melanoma diagnosis.

Purpose of the Study:

To develop a novel, optimized approach for the early and accurate detection of melanoma.
To leverage advanced machine learning techniques for enhanced skin cancer diagnosis.

Main Methods:

The study employed reinforcement learning for precise segmentation of skin regions.
Feature extraction and selection were performed using the Enhanced Fish Migration Optimizer (EFMO) algorithm.
Classification of melanoma was achieved using a Support Vector Machine (SVM) optimized by the EFMO algorithm.

Main Results:

The proposed method demonstrated superior performance in melanoma detection.
Validation on the SIIM-ISIC Melanoma dataset showed the method outperformed 12 other existing approaches.
The EFMO algorithm effectively optimized both feature selection and SVM classification.

Conclusions:

The developed approach offers a promising, highly accurate, and efficient tool for early melanoma detection.
This AI-driven method has the potential to significantly aid dermatologists in diagnosing skin cancer.
The integration of reinforcement learning and metaheuristic optimization presents a powerful strategy for medical image analysis.