Intelligent Multi Class Skin Disease Detection System

Skin diseases impact millions of people worldwide, covering diverse demographic groups and represent a substantial public health issue. In order to effectively treat and manage skin disorders, prompt and accurate diagnosis is essential. Deep learning algorithms have accelerated the development of computer aided diagnosis systems, allowing for extremely accurate automated interpretation of medical images. Integrating these algorithms with Raspberry Pi gives medical professionals a low cost, portable way to diagnose patients in real time especially in places with limited resources and access to advanced medical technology. The objective of the proposed research work is to address the problem of multiclass skin disease detection, in which a system is trained to simultaneously distinguish between multiple skin disorders. Multiclass classification involves detecting a wide range of dermatological conditions such as psoriasis, eczema, and melanoma, in contrast to binary classification tasks that distinguish between healthy and diseased skin. The suggested system intends to offer dermatologists and primary care physician comprehensive support in accurately diagnosing a wide range of skin disorders by expanding the scope of detection. It combines deep learning methods with an intelligent embedded system that makes use of a Raspberry Pi to automatically categorize various skin conditions. With the use of an embedded Pi camera and a deep convolutional neural network model, the system is meant to recognize and categorize skin images in real time. Therefore, the suggested work provides an accessible and affordable method of accurately identifying a range of skin conditions. The combination of image processing and machine learning algorithms incorporated in the system enable to classify a number of skin diseases with a high degree of accuracy, enabling prompt diagnosis and treatment.