SKIN DISEASE CLASSIFICATION USING DEEP LEARNING

Abstract

Skin diseases are a major public health problem around the world. Millions upon millions of people suffer from them. Accurate and timely diagnosis is key to effective treatment of skin conditions. In this paper, we introduce a Deep Neural Networks (DNN) based Skin Disease Classification System. This proposed system employs machine learning to automatically categorize skin diseases from dermatological images. Using a deep learning model trained on an extensive collection of dermatological images is the focus of our study. The scope of the data set is broad and covers a variety of skin conditions, which allows the model to recognize complex patterns and characteristics related to different illnesses. We look at the possibility of using Convolution Neural Networks (CNNs) as a more specialized form of DNN to infer spatial hierarchies in skin images, so that we can bring out this fine distinction between diseases. In the training process, it is necessary to fine tune DNN so that its classification accuracy for skin diseases can reach a high level. Accuracy, precision, recall and F1 score are used to assess the achievements of our system. The results show that the DNN model is an effective tool for differentiating between skin conditions. A useful tool for dermatologists and healthcare professionals to augment their diagnostic capabilities The proposed Skin Disease Classification System therefore represents an advance in the field. Such an automated system makes rapid and consistent diagnosis possible, which should help cut the time to take treatment decision. In addition, the application of deep learning methods makes for scalability and flexibility. The model can automatically train itself as it is being exposed to further data in order to maintain its present level of accuracy or increase that level still more.