A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features.

Autor: Zareen SS; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China., Guangmin S; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China., Li Y; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China., Kundi M; Department of Informatics, University of Leicester, Leicester, UK., Qadri S; Department of Computer Science, Muhammad Nawaz Shareef University of Agriculture, Multan 66000, Pakistan., Qadri SF; Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou 311121, China., Ahmad M; Faculty of Computer Science and Technology, University of Lahore, Sargodha, Pakistan., Khan AH; Department of Computer Science, School of Systems & Technology, University of Management and Technology, Lahore, Pakistan.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jul 18; Vol. 2022, pp. 4942637. Date of Electronic Publication: 2022 Jul 18 (Print Publication: 2022).
DOI: 10.1155/2022/4942637
Abstrakt: The main purpose of this study is to observe the importance of machine vision (MV) approach for the identification of five types of skin cancers, namely, actinic-keratosis, benign, solar-lentigo, malignant, and nevus. The 1000 (200 × 5) benchmark image datasets of skin cancers are collected from the International Skin Imaging Collaboration (ISIC). The acquired ISIC image datasets were transformed into texture feature dataset that was a combination of first-order histogram and gray level co-occurrence matrix (GLCM) features. For the skin cancer image, a total of 137,400 (229 × 3 x 200) texture features were acquired on three nonover-lapping regions of interest (ROIs). Principal component analysis (PCA) clustering approach was employed for reducing the dimension of feature dataset. Each image acquired twenty most discriminate features based on two different approaches of statistical features such as average correlation coefficient plus probability of error (ACC + POE) and Fisher (Fis). Furthermore, a correlation-based feature selection (CFS) approach was employed for feature reduction, and optimized 12 features were acquired. Furthermore, a classification algorithm naive bayes (NB), Bayes Net (BN), LMT Tree, and multilayer perception (MLP) using 10 K-fold cross-validation approach were employed on optimized feature datasets and the overall accuracy achieved by MLP is 97.1333%.
Competing Interests: The authors declare no conflicts of interest.
(Copyright © 2022 Syeda Shamaila Zareen et al.)
Databáze: MEDLINE
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