An Automatic Dermatology Detection System Based on Deep Learning and Computer Vision

Automatic medical diagnosis has gained significant attention among researchers, particularly in disease diagnosis. Differentiating between dermatology diseases is pivotal in clinical decision-making as it provides prognostic and predictive information and treatment strategies. This paper proposes a dermatology detection system based on deep learning (DL) and object recognition. The proposed model consists of three phases: Data preprocessing, data augmentation, and classification with localization. In the data preprocessing phase, we apply various operations such as color transformation, resizing, normalization, and labeling to prepare the input image for enrollment in our DL models. The data augmentation phase is carried out on the input images using the convolutional generative adversarial network algorithm. In the third phase, YOLO-V5 is used to classify and localize objects. The dataset is carefully collected with the assistance of medical specialists to ensure its accuracy. The proposed models are evaluated and compared using various metrics. Our empirical results demonstrate that the proposed model outperforms state-of-the-art models in terms of accuracy. Our proposed methodology offers significant improvements in detecting vitiligo and melanoma compared to recent techniques.