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

An Effective Multiclass Human Skin Lesion Diagnosis System Based on Convolutional Neural Networks 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%). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

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

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References (17)

Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2023. ISSN 0146-4116, Automatic Control and Computer Sciences, 2023, Vol. 57, No. 2, pp. 135–142. © Allerton Press, Inc., 2023.
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411623020025
Publisher site
See Article on Publisher Site

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%).

Journal

Automatic Control and Computer SciencesSpringer Journals

Published: Apr 1, 2023

Keywords: multiclass human skin lesion; convolutional neural networks (CNNs); diagnosis system; dermoscopic images

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