Kingsley A. Ogudo¹, R. Surendran^2,*, Osamah Ibrahim Khalaf³

Computer Systems Science and Engineering, Vol.44, No.1, pp. 693-707, 2023, DOI:10.32604/csse.2023.024154 - 01 June 2022

Abstract Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep… More >

Romany F. Mansour^1,*, Sara A. Althubiti², Fayadh Alenezi³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 849-864, 2022, DOI:10.32604/cmc.2022.029265 - 18 May 2022

Abstract 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 More >

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

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1617-1630, 2022, DOI:10.32604/cmc.2022.018621 - 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… More >