SKIN DISEASE DETECTION

Abstract

The goal of this project is to create picture recognition algorithms that can identify and categorise skin conditions from input photographs, offering information about possible therapies and management approaches. Several image processing methods, such as feature extraction, segmentation, and preprocessing. Convolutional neural networks, or CNNs, are used because they are well-suited for tasks involving feature extraction and classification, especially in the field of image recognition. The system's capacity to quickly and accurately diagnose patients based on image analysis forms its foundation. The system uses deep learning techniques to analyse important aspects and patterns in images submitted to it, allowing it to reliably identify a variety of skin conditions. It then looks up possible illnesses and suggested remedies in a database compiled by dermatologists. Additionally, the system has a reminder function to handle the critical component of treatment adherence. In order to guarantee consistency and effectiveness in the management of their diseases, users are promptly reminded to take their prescribed medications. For those with skin conditions, this proactive approach improves treatment compliance and leads to better health results. Our project offers a comprehensive and user-friendly solution for the detection and management of skin diseases, marking a significant leap in the field of dermatology. Our goal is to enable prompt access to professional medical advice and treatment while empowering people to take charge of their skin health by fusing state-of-the-art technology with in-depth medical expertise