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

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...

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Bibliographic Details
Published in:Computer assisted methods in engineering and science Vol. 30; no. 2
Main Authors: Dibyahash Bordoloi, Vijay Singh, Karthikeyan Kaliyaperumal, Mahyudin Ritonga, Malik Jawarneh, Thanwamas Kassanuk, Jose Quiñonez-Choquecota
Format: Journal Article
Language:English
Published: Institute of Fundamental Technological Research Polish Academy of Sciences 01.04.2023
Subjects:
classification
disease detection
image enchancement
image segmentation
machine learning
skin disorders
ISSN:2299-3649, 2956-5839
Online Access:Get full text
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Summary: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%.
ISSN:2299-3649
2956-5839
DOI:10.24423/cames.479