Open Access

ARTICLE

A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

Khadija Manzoor¹, Fiaz Majeed², Ansar Siddique², Talha Meraj³, Hafiz Tayyab Rauf^4,*, Mohammed A. El-Meligy⁵, Mohamed Sharaf⁶, Abd Elatty E. Abd Elgawad⁶

1 Department of Information Technology, University of Gujrat, Gujrat, 50700, Pakistan
2 Department of Software Engineering, University of Gujrat, Gujrat, 50700, Pakistan
3 Department of Computer Science, COMSATS University Islamabad - Wah Campus, Wah Cantt, 47040, Pakistan
4 Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent, UK
5 Advanced Manufacturing Institute, King Saud University, Riyadh, 11421, Saudi Arabia
6 Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia

* Corresponding Author: Hafiz Tayyab Rauf. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2022, 70(1), 1617-1630. https://doi.org/10.32604/cmc.2022.018621

Received 14 March 2021; Accepted 16 April 2021; Issue published 07 September 2021

Abstract

Skin diseases effectively influence all parts of life. Early and accurate detection of skin cancer is necessary to avoid significant loss. The manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color levels. Therefore, an automated system to identify these skin diseases is required. Few studies on skin disease classification using different techniques have been found. However, previous techniques failed to identify multi-class skin disease images due to their similar appearance. In the proposed study, a computer-aided framework for automatic skin disease detection is presented. In the proposed research, we collected and normalized the datasets from two databases (ISIC archive, Mendeley) based on six Basal Cell Carcinoma (BCC), Actinic Keratosis (AK), Seborrheic Keratosis (SK), Nevus (N), Squamous Cell Carcinoma (SCC), and Melanoma (M) common skin diseases. Besides, segmentation is performed using deep Convolutional Neural Networks (CNN). Furthermore, three types of features are extracted from segmented skin lesions: ABCD rule, GLCM, and in-depth features. AlexNet transfer learning is used for deep feature extraction, while a support vector machine (SVM) is used for classification. Experimental results show that SVM outperformed other studies in terms of accuracy, as AK disease achieved 100% accuracy, BCC 92.7%, M 95.1%, N 97.8%, SK 93.1%, SCC 91.4% with a global accuracy of 95.4%.

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