Skin disease detection using vision transformers

Skin diseases are prevalent health issues that significantly impact individual's quality of life. Early and accurate diagnosis is crucial for timely treatment, leading to faster recovery. With advancements in machine learning and computer vision, Vision Transformers (ViTs) have emerged as a powerful alternative to Convolutional Neural Networks (CNNs) for automatic skin disease detection. This study explores the application of Vision Transformers in diagnosing skin diseases, highlighting their potential to support dermatologists and healthcare professionals. The proposed method utilizes the HAM10000 image dataset comprising various skin conditions, including melanoma, benign keratosis, basal carcinoma and other common ailments. Vision Transformers, known for their ability to capture long-range dependencies and global context in images, are employed to extract high-level features from input images. These features are then fed into a classification layer for disease detection. The ViT model learns to identify patterns associated with different skin diseases through training on an extensive dataset of skin images. When presented with a new image, the model extracts relevant features, enabling it to accurately classify the disease. The test accuracy and val loss are 93.36% and 0.2181. This study demonstrates the effectiveness of Vision Transformers in skin disease detection, offering a promising tool for improving diagnostic accuracy and supporting early intervention in dermatology.