Cloud-Based Skin Cancer Classification: Training and Deploying a Model on AWS

Skin cancer has become one of the most dangerous and most common types of cancer in recent years. Skin cancers come in a variety of types, and identifying the type is crucial for treating the condition when it is still treatable. The dermatologist must also distinguish between skin conditions that affect the tissues on the top layer of the skin and cells of the skin cancer that develop in the epidermal layer of the skin. The current methods for identifying or categorizing skin cancer take a long time and can be painful for the patient due to potential side effects. There is extensive research going on in this area but the unavailability of the balanced datasets and small size of the datasets have become a hindrance. There are not many products like web applications which use deep learning models to identify and categorize the type of skin cancer. We have used the ConvNeXt Tiny deep learning model which is pre-trained on ImageNet. Once we had obtained good accuracy, we had used that model and created a web application which is hosted in the AWS Cloud. The dataset we used for our work is ISIC2018. It consists of seven classes of dermoscopic images of skin lesions which are of high resolution. It is an imbalanced data that has images collected from various clinical sites.