Identification and Classification of Skin Diseases using Deep Learning Techniques

Abstract Health is the most important aspect of a human’s life. Humans are covered by the largest organ of their body, the skin. Protecting the body from germs and regulating body temperature are the basic functionalities of the skin. If the skin is not protected, then it may lead to skin diseases caused by bacterial infections, fungal infections, etc. The cost involved in diagnosing skin diseases may not seem cost-effective to everyone. Moreover, dermatologists need to use their professional experience and may have to invest more time in diagnosing skin diseases which usually involves the use of images captured using a dermoscope. The proposed methodology adopts Deep Learning techniques for identifying and classifying skin diseases caused by bacteria and fungi by making use of non-dermoscopic images. The problem statement is an image classification task. The dataset consists of five classes which include two classes of bacterial skin disease namely Cellulitis and Impetigo, and two classes of fungi skin diseases namely Ringworm and Sporotrichosis. Healthy Skin images are the fifth class in the dataset. The Convolutional Neural Network (CNN) is known to be the most suitable deep learning algorithm for image processing applications. The power of the transfer learning technique is utilized by applying the fine-tuned version of VGG16 architecture. A web application is developed using Streamlit to provide a smooth user interface for displaying the predicted result of a skin image. The web app facilitates a user to either upload an image or captures a real-time image. The developed solution achieves an accuracy of 86% and an F1-score of 85%. Such a deep learning model will assist dermatologists in decision making, reduce the time required for diagnosis, and also minimizes the cost involved by a significant amount.