Abstract: Skin diseases are a global health problem that is difficult to diagnose sometimes due to the disease’s complexity, and the time-consuming effort. In addition to the fact that skin diseases affect human health, it also affects the psycho-social life if not diagnosed and controlled early. The enhancement of images processing techniques and machine learning leads to an effective and fast diagnosis that help detect the skin disease early. This paper presents a model that takes an image of the skin affected by a disease and diagnose acne, cherry angioma, melanoma, and psoriasis. The proposed model is composed of five steps, i.e., image acquisition, preprocessing, segmentation, feature extraction, and classification. In addition to using the machine learning algorithms for evaluating the model, i.e., Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (K-NN) classifiers, and achieved 90.7%, 84.2%, and 67.1%, respectively. Also, the SVM classifier result of the proposed model was compared with other papers, and mostly the proposed model’s result is better. In contrast, one paper achieved an accuracy of 100%.

Shaden Abdulaziz AlDera and Mohamed Tahar Ben Othman, “A Model for Classification and Diagnosis of Skin Disease using Machine Learning and Image Processing Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130531

@article{AlDera2022,
title = {A Model for Classification and Diagnosis of Skin Disease using Machine Learning and Image Processing Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130531},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130531},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Shaden Abdulaziz AlDera and Mohamed Tahar Ben Othman}
}