Dermatological disease detection and preventative measures using deep convolution neural networks.

Skin disease is the most uncertain medical issue to detect. Mostly, it is extremely difficult to recognise skin disease with the naked eye. Before we go to a dermatologist, it is better to have self-care by knowing the kind. There are hundreds of categories of skin diseases. A few of them look very similar. We make wrong identification and implement incorrect measures that result in getting wrong results and also harming ourselves. So, to solve this problem, a new application was developed. It takes the image as input, and the name of the disease and control measures are displayed as output. The application takes the input image and processes it with the dataset already given and categorises the disease. The name of the disease and tips to reduce the spread of the disease are given to the user. Python code is used for the processing of images, and we employ a method called deep convolution neural networks (CNN). We are categorising the given image into four types: acne, benign, malignant, and skin allergy. The datasets are collected from different sources, such as Kaggle, GitHub, and Google. There are approximately 3003 training datasets and 600 test datasets in total. This network will give an accuracy of 98.7% and will produce results quicker than the formal technique, making this submission an effective and reliable system for dermatologic disease detection. Biomedical students in the dermatology branch can also use this as a consistent real-time training tool. [ABSTRACT FROM AUTHOR]