SKIN DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORKS

Abstract— Dermatology is the branch of bioscience that deals with treating and diagnosing skin disorders and allergies that can be caused due to various geographical and biological factors. However, human skin is a complex and unpredictable domain to define, synthesize and analyze and to rather to distinguish disorders affecting with skin. To address this issue, Researchers are still working to figure out and model human skin behaviour to automate the process of skin disease detecting through victimization that uses vision techniques to meet the criteria of medical science. Patients in remote areas where access to quality medical facilities is still limited may ignore symptoms and exacerbate their conditions. Automated medical paradigms are necessary to effectively address these issues and provide convenient and accessible solutions to people's needs. The emergence of automated paradigms for detecting skin diseases is a promising solution to ensure accurate diagnosis of skin-related issues. A multiclass deep learning model has been proposed to develop that can differentiate between healthy skin and skin affected by a disease, and also categorize various types of skin diseases. We used Deep Learning, a type of machine learning that uses large datasets to reduce the number of classifiers required to train our model. This approach lets the machine to learn and divide the provided data into levels of predictions and in a very short period of time gives delivers accurate results, thus our work aims to promote and support development of Dermatology. The algorithm we used here is Convolutional Neural Network (CNN) as it is one of the most preferred algorithms for image classification