Classification and Detection of Skin Disease Based on Machine Learning and Image Processing Evolutionary Models

  • Dibyahash Bordoloi Department of Computer Science & Engineering, Graphic Era Deemed to be University, India
  • Vijay Singh Department of Computer Science & Engineering, Graphic Era Deemed to be University, India
  • Karthikeyan Kaliyaperumal Department of Information Technology, School of Informatics and Electrical Engineering, Ambo University, Ethiopia
  • Mahyudin Ritonga Universitas Muhammadiyah Sumatera Barat, Indonesia
  • Malik Jawarneh Faculty of Computing Sciences, Gulf College, Oman
  • Thanwamas Kassanuk Pibulsongkram Rajabhat University, Phitsanulok, Thailand
  • Jose Quiñonez-Choquecota Universidad Nacional del Altiplano, Peru


Skin disorders, a prevalent cause of illnesses, may be identified by studying their physical structure and history of the condition. Currently, skin diseases are diagnosed using invasive procedures such as clinical examination and histology. The examinations are quite effective and beneficial. This paper describes an evolutionary model for skin disease classification and detection based on machine learning and image processing. This model integrates image preprocessing, image augmentation, segmentation, and machine learning algorithms. The experimental investigation makes use of a dermatology data set. The model employs the machine learning methods: the support vector machine (SVM), the k-nearest neighbors (KNN), and random forest algorithms for image categorization and detection. This suggested methodology is beneficial for the accurate identification of skin disease using image analysis. The SVM algorithm achieved an accuracy of 98.8%. The KNN algorithm achieved a sensitivity of 91%. The specificity of KNN was 99%.


skin disorders, machine learning, classification, image enhancement, image segmentation, disease detection,


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Apr 21, 2023
How to Cite
BORDOLOI, Dibyahash et al. Classification and Detection of Skin Disease Based on Machine Learning and Image Processing Evolutionary Models. Computer Assisted Methods in Engineering and Science, [S.l.], v. 30, n. 2, p. 247–256, apr. 2023. ISSN 2956-5839. Available at: <>. Date accessed: 17 apr. 2024. doi: