Skin Disease Detection System Using CNN

Skin disease among humans has been a common disease, millions of people are suffering from various kinds of skin diseases. Usually, these diseases have hidden dangers which lead to not only a lack of self-confidence and psychological depression but also lead a risk of skin cancer. According to World Health Organization (WHO), around 30% to 70% of the population has fallen victim to skin disease. And most of these individuals don’t know much about the classification of skin disease.

To tackle the above-mentioned problem, we have designed a Skin Disease Detection System Using CNN. The idea behind this project is to make it possible for the common man to get a sense of the disease affecting his/her skin so they can get a head start in preparing for its betterment and also the doctor in charge can get an idea about the type of cancer, which ultimately helps in faster and efficient diagnosis.

The user would need to register first to log into the system. After logging in, the user would need to upload the image and the system will automatically detect the class of the skin disease that seems to appear on the image. The user can also view doctors for the diagnosis. The system will show the doctors as per the class of disease detected. The user can also give feedback.

The admin can log in using their credentials. They can view users and have the access to add, update, delete and view doctors. They can also view the feedback given by the users.
We have made use of a Convolutional Neural Network that uses Batch Normalization to normalize the layer’s inputs and also makes use of an Adam optimizer. The dataset used is from Kaggle. The system will detect the following diseases: Acne, Rosacea, Actinic Keratosis, Basal Cell Carcinoma, Melanoma Skin Cancer, Dysplastic Nevi, Moles and other Malignant Lesions.

Advantages

• The system is easy to maintain.
• It is user-friendly.
• It automatically detects without any human supervision.
• A simple and effective method for identifying a person’s signature.