A Comparative Study of Artificial Intelligence Classification Models for Analyzing Vitiligo Effects in Zebrafish (Danio rerio) Images

In recent years, the term Artificial Intelligence (AI) has gained recognition across various fields, such as healthcare, finance, agriculture, and many other sectors due to its versatility and capacity to improve outcome. Within the applications of AI, classification stands out as one of the most prevalent, aiding in optimizing decision-making and efficiently organizing data. In recent years, there has been an increase in the use of zebrafish (Danio rerio) in studies related to human dermatological diseases, such as vitiligo, which involves the autoimmune-mediated destruction of the melanocytes in the epidermal layer. Despite the current interest in studies related to this disease, no papers were found applying Machine Learning (ML) or Deep Learning (DL) models to classify the effects of the disease and its treatments. In this context, this paper uses the challenge of evaluating the effects of vitiligo in zebrafish to compare the performance of different AI approaches. The methodology employed in this paper includes image acquisition, dataset creation, preprocessing, model testing, and evaluation. The ML models applied in this study were Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), while the DL models included Visual Geometry Group 16 (VGG16) and GoogLeNet. Following the evaluation, SVM and GoogLeNet achieved the best results, correctly classifying 80% and 71% of the data, respectively. Moreover, the former accurately identified all samples in the healthy and treated classes, with misclassifications occurring only within the sick class. The models performed satisfactorily in relation to the objectives of this study and the results exhibited potential for future applications in treating vitiligo in humans.