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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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Optimized attention-induced multihead convolutional neural network with efficientnetv2-fostered melanoma

M Maheswari¹, Mohamed Uvaze Ahamed Ayoobkhan², C P Shirley³

¹Department of Information Technology, DMI College of Engineering, Chennai, Tamil Nadu, India. maheshwari.mnsnew@gmail.com.

Medical & Biological Engineering & Computing

|June 4, 2024

Summary

This study introduces an AI model for melanoma detection using dermoscopic images. The Optimized Attention-Induced Multihead Convolutional Neural Network with EfficientNetV2 (AIMCNN-ENetV2-MC) achieves high accuracy in classifying melanoma and benign nevi.

Keywords:

Attention-induced multihead convolutional neural network Boosted chimp optimizer Dermoscopic images dataset EffiectiveNetV2

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Melanoma is a dangerous skin cancer requiring early detection.
Dermoscopic imaging aids diagnosis, but distinguishing melanoma from other conditions is challenging.
Manual diagnosis is time-consuming and requires expert dermatologists.

Purpose of the Study:

To develop an automated system for accurate melanoma classification from dermoscopic images.
To improve the efficiency and accuracy of melanoma diagnosis using deep learning.

Main Methods:

Proposed an Optimized Attention-Induced Multihead Convolutional Neural Network with EfficientNetV2 (AIMCNN-ENetV2-MC).
Utilized Adaptive Distorted Gaussian Matched Filter (ADGMF) for image pre-processing.
Optimized the classifier using the Boosted Chimp Optimization Algorithm (BCOA).

Main Results:

Achieved an overall accuracy of 98.75% in classifying acral melanoma and benign nevi.
Demonstrated a reduced computation time of 98 seconds compared to existing models.
The AIMCNN-ENetV2-MC model showed superior performance in melanoma classification.

Conclusions:

The proposed AIMCNN-ENetV2-MC model offers a highly accurate and efficient solution for melanoma detection.
Automated classification using deep learning can significantly aid dermatologists in early melanoma identification.
This AI-driven approach has the potential to improve patient outcomes through timely diagnosis.