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Computer Vision with Machine Learning Enabled Skin Lesion Classification Model

Romany F. Mansour1,*, Sara A. Althubiti2, Fayadh Alenezi3

1 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt
2 Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
3 Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia

* Corresponding Author: Romany F. Mansour. Email:

Computers, Materials & Continua 2022, 73(1), 849-864.


Recently, computer vision (CV) based disease diagnosis models have been utilized in various areas of healthcare. At the same time, deep learning (DL) and machine learning (ML) models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools. This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification (CVOML-SLDC) model. The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images. Primarily, the CVOML-SLDC model derives a gaussian filtering (GF) approach to pre-process the input images and graph cut segmentation is applied. Besides, firefly algorithm (FFA) with EfficientNet based feature extraction module is applied for effectual derivation of feature vectors. Moreover, naïve bayes (NB) classifier is utilized for the skin lesion detection and classification model. The application of FFA helps to effectually adjust the hyperparameter values of the EfficientNet model. The experimental analysis of the CVOML-SLDC model is performed using benchmark skin lesion dataset. The detailed comparative study of the CVOML-SLDC model reported the improved outcomes over the recent approaches with maximum accuracy of 94.83%.


Cite This Article

R. F. Mansour, S. A. Althubiti and F. Alenezi, "Computer vision with machine learning enabled skin lesion classification model," Computers, Materials & Continua, vol. 73, no.1, pp. 849–864, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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