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Automatic Control and Computer Sciences

Published in:

01-04-2023

An Effective Multiclass Human Skin Lesion Diagnosis System Based on Convolutional Neural Networks

Authors: Ahmed A. Alani, Hayder G. A. Altameemi, Ahmed Abdul Azeez Asmael, Mudhar A. Al-Obaidi

Published in: Automatic Control and Computer Sciences | Issue 2/2023

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Abstract

Recently, deep learning algorithms have acquired considerable attention to diagnosing different human diseases. Hence, recent researches prove the efficiency of these algorithms in skin lesions diagnosis using dermoscopic images. However, the situation of multiclass skin lesions is not taken into consideration via most of such researches. In this paper, an effective system of multiclass human skin lesion diagnosis based on convolutional neural networks (CNNs) is proposed. This proposed system is designed with multilayers, implemented, and calibrated for classifying the images of skin lesions into seven categories: basal cell carcinoma, actinic keratoses, dermatofibroma, benign keratosis, vascular, melanocytic nevi, and melanoma skin lesions. The proposed CNN based diagnosis system is evaluated via the experiments on the HAM10000 dataset using different terms. The obtained results illustrate that the proposed diagnosis system exceeds most of the recent existing systems, depending on the chosen terms involving precision (84%), recall (82%), F1-score (81%), and accuracy (95%).

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Title: An Effective Multiclass Human Skin Lesion Diagnosis System Based on Convolutional Neural Networks
Authors: Ahmed A. Alani
Hayder G. A. Altameemi
Ahmed Abdul Azeez Asmael
Mudhar A. Al-Obaidi
Publication date: 01-04-2023
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 2/2023
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411623020025

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Abstract

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