Automated Multiclass Skin Disease Diagnosis using Deep Learning

Abstract

The use of deep learning techniques for illness diagnosis has emerged as a new area of medical research interest. Dermatoses are among the most prevalent medical conditions, and compared to other disease kinds, they are easier to see visually. Thus, using deep learning techniques to the identification of skin conditions from images is very important and has drawn interest from researchers. Due to the lack of medical facilities in remote areas, patients tend to ignore early symptoms and the condition may worsen over time. Therefore, there is an increasing demand for automated systems for detecting skin diseases with high accuracy. In order to categorize skin illnesses and distinguish between healthy and unhealthy skin, we consequently created a multi-class deep learning model. Deep learning techniques have brought about a revolution in medical research, particularly in the area of disease diagnosis. Within the medical landscape, skin diseases represent a prevalent health concern, often distinguished by their prominent visual manifestations. Consequently, the application of deep learning techniques to facilitate accurate skin disease image recognition has garnered substantial attention from the research community. Compounded by the lack of accessible medical facilities in remote regions, early symptoms of skin diseases often go unnoticed by patients, potentially exacerbating their conditions over time. Consequently, the imperative for a high-precision automated skin disease detection system has become increasingly apparent. Given these challenges, we propose a comprehensive multi-class deep learning model designed to differentiate between healthy skin and disease-affected skin and classify specific skin diseases. Our approach integrates a robust dataset, meticulously curated and preprocessed to ensure optimal model performance. Leveraging a carefully constructed deep learning architecture, our model achieves notable accuracy and efficiency in distinguishing various skin conditions. Through extensive experimentation and evaluation, we demonstrate the efficacy of our proposed model, highlighting its ability to accurately classify and diagnose a range of common skin diseases. This research contributes significantly to the ongoing efforts to enhance early detection and intervention in skin disease management, particularly in underserved regions. By presenting a reliable and accessible automated system, we aim to mitigate the challenges associated with timely diagnosis and treatment, ultimately improving patient outcomes and alleviating the burden on healthcare systems.