A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

• Published in: Computers, materials & continua materials & continua, 2022-01, Vol.70 (1), p.1617-1630
• Main Authors: Manzoor, Khadija, Majeed, Fiaz, Siddique, Ansar, Meraj, Talha, Tayyab Rauf, Hafiz, A. El-Meligy, Mohammed, Sharaf, Mohamed, Elatty E. Abd Elgawad, Abd
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Cancer; Classification; Feature extraction; Image segmentation; Skin cancer; Skin diseases; Support vector machines
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2022.018621

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%.