Dermatological Categorization Using Deep Learning

Abstract

Abstract: Skin disease is a rampant medical condition in today's society that demands immediate attention. The manifestation of small circular or irregularly shaped spots on the skin is a clear indication of skin disease. If left untreated, skin disease could progress into skin cancer, a condition that poses a great danger to the patient's life. This project is set to detect possible signs of skin abrasions or infections. By utilizing neural networks or machine learning algorithms to scour an open-source dataset of skin diseases, the project provides the most accurate match to the patient's likely condition. We will use CNN transfer learning techniques, particularly VGG19 or different Resnet approaches, to ensure maximum accuracy. Our goal is to cover at least 12+ out of the 23 available classes and achieve an accuracy rate of 90% or higher on the training set and 85% or higher on the validation set. The project will also include the best available treatments to treat any detected disease with a tailored dataset. We are committed to providing top-notch medical care and ensuring the best possible outcome for our patients.