Dermcare - Skin Disease Classification from Image

Being one of the most exposed organs, the human skin acts as the body's main line of protection against microbiological dangers. Nevertheless, systematic approaches to product utilization and skincare regimens are still largely unexplored. Due to the variety and dynamic nature of skin illnesses, as well as their rising frequency among skin types from different cultural backgrounds, effective methods of diagnosis and treatment are required. The current system for diagnosing skin diseases is complicated and mainly depends on expert medical opinion and information from skin biopsies. Objective: The goal of the study is to increase the accuracy of diagnosis while overcoming the difficulties in differentiating between skin disorders that appear physically identical. By lowering diagnosis time and encouraging more practical and accurate treatment options, this method aims to improve patient care. Methodology: The classification model for skin diseases will be created using a convolutional neural network. The preprocessed picture dataset will be used to train the newly created CNN model. The model's parameters will need to be optimized during the training process. A separate validation dataset will be used to test the trained model's effectiveness in correctly categorizing various skin conditions. Results: The proposed deep learning-based skin disease classification system demonstrated promising results in accurately diagnosing various skin conditions. The model achieved an overall accuracy of 91 % on the validation dataset, indicating its effectiveness in distinguishing between different diseases.