Open Access

ARTICLE

Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

Amani Abdulrahman Albraikan¹, Nadhem NEMRI², Mimouna Abdullah Alkhonaini³, Anwer Mustafa Hilal^4,*, Ishfaq Yaseen⁴, Abdelwahed Motwakel⁴

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
3 Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
4 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computers, Materials & Continua 2023, 74(2), 2443-2459. https://doi.org/10.32604/cmc.2023.026379

Received 23 December 2021; Accepted 24 January 2022; Issue published 31 October 2022

Abstract

Melanoma remains a serious illness which is a common form of skin cancer. Since the earlier detection of melanoma reduces the mortality rate, it is essential to design reliable and automated disease diagnosis model using dermoscopic images. The recent advances in deep learning (DL) models find useful to examine the medical image and make proper decisions. In this study, an automated deep learning based melanoma detection and classification (ADL-MDC) model is presented. The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma. The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage. Besides, the k-means clustering technique is applied for the image segmentation process. In addition, Adagrad optimizer based Capsule Network (CapsNet) model is derived for effective feature extraction process. Lastly, crow search optimization (CSO) algorithm with sparse autoencoder (SAE) model is utilized for the melanoma classification process. The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance. A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects. The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.

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Keywords

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