Preprocessed Vision Transformers and Classical Classifiers in Diagnosing Skin Diseases

Vision Transformers (ViTs) are the state-of-the-art deep learning technology in medicine. ViTs require a large number of parameters, so they need a relatively large dataset for learning. This is currently possible due to the digitization of healthcare. As a comparison, we also use classical classifiers, which are characterized by a relatively low number of input data. In clinical practice, high-resolution images such as those from dermoscopy, confocal microscopy, reflectance confocal microscopy, and Raman spectroscopy are used to diagnose skin diseases. ViTs have potential in clinical practice. The advantage of the model over convolutional neural networks is that they do not use convolutional operations. Preprocessed images from a dataset were classified experimentally using five ViTs models of various sizes and respective classical classifiers. Comparative experiments were conducted also on preprocessed dermatoscopic images from another dataset. This article introduces an artificial intelligence method for identifying various skin conditions. The dataset contains images that are classified into 5 categories: normal, melanoma, arsenic, psoriasis, and eczema. During the study, skin images underwent initial processing using the Adaptive Histogram Equalization (AHE) technique, which enhanced the contrast to reveal important details. Following this preprocessing, features were obtained from the images using ViTs, renowned for their ability to capture intricate visual information. These extracted features were then utilized in conjunction with traditional machine learning classifiers, resulting in accurate diagnosis of the skin conditions being studied. The findings emphasize the effectiveness of combining ViTs with classical classifiers in tasks related to medical image classification.