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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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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images.

Thavavel Vaiyapuri¹, Prasanalakshmi Balaji², Shridevi S³

¹College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz Univeristy, Al Kharj, Saudi Arabia.

Computational Intelligence and Neuroscience

|June 10, 2022

Summary

This study introduces a new computational intelligence-based melanoma detection and classification technique (CIMDC-DI) using dermoscopic images. The novel method achieves high accuracy, improving early skin cancer diagnosis.

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Area of Science:

Dermatology and Computational Intelligence
Medical Imaging and Artificial Intelligence

Background:

Melanoma detection is challenging due to low contrast and lesion similarity.
Intelligent computer-aided diagnosis (CAD) models are crucial for accurate melanoma identification.
Computational intelligence (CI) and deep learning (DL) are increasingly vital in biomedical applications.

Purpose of the Study:

To develop a novel computational intelligence-based melanoma detection and classification technique using dermoscopic images (CIMDC-DI).
To enhance the accuracy and efficiency of melanoma diagnosis through advanced AI methods.

Main Methods:

Image segmentation using bilateral filtering and fuzzy k-means (FKM) clustering.
Feature extraction via NasNet with stochastic gradient descent.
Classification using manta ray foraging optimization (MRFO) algorithm and a cascaded neural network (CNN).

Main Results:

The proposed CIMDC-DI technique demonstrated significant effectiveness in melanoma detection and classification.
Achieved a maximum accuracy of 97.50% in simulation analysis.
Outperformed existing recent algorithms in performance.

Conclusions:

The CIMDC-DI model offers a promising approach for accurate melanoma diagnosis from dermoscopic images.
CI and DL techniques integrated into CAD systems can significantly improve skin cancer detection rates.
This research highlights the potential of advanced AI for clinical decision support in dermatology.