Authors :Nitish Kumar , Nishant Kumar, Rahul Kumar, Dr. R. Sudhakar
DOI : 10.46335/IJIES.2025.10.8.9
Abstract – Diagnosing skin diseases can be challenging with traditional methods, as they rely on manual examination and a doctor's expertise, which may lead to errors or delays. This project introduces an advanced approach by integrating Convolutional Neural Networks (CNNs) and Markov Decision Processes (MDPs) to improve skin disease detection and treatment recommendations. CNNs are powerful deep learning models designed for image analysis. In this project, they are trained on large datasets like HAM10000 and ISIC Archive, which contain thousands of skin disease cases. By learning patterns in images, CNNs accurately identify different skin conditions based on features such as unusual spots, textures, or rashes. While CNNs classify the disease, the MDP component enhances decision-making by analyzing potential outcomes and suggesting the best course of action for treatment. Acting as a decision-support tool, MDPs evaluate different possibilities and recommend suitable diagnostic or therapeutic steps. By combining CNNs and MDPs, this approach not only increases the accuracy of skin disease detection but also assists doctors in making informed decisions. This innovative system overcomes the limitations of traditional methods and contributes to better clinical outcomes for patients.
