A Custom Image Recognition Model in the Cloud for Skin Diseases Identification

In the context of global warming and increasing exposure to UV radiation, skin diseases are becoming more prevalent. Some of the most widespread skin conditions are solar lentigo and actinic keratosis. In this paper, we propose a technical approach related to the use of Azure Custom Vision services to classify these two conditions. The main advantage of using this service is the computational power offered by Azure. Additionally, generating a convolutional neural network model does not require a large dataset to achieve a good performance. For training the model, we used a dataset of 600 images from the ISIC database. The limitations of these approaches are imposed by the manual image labeling part that needs to be performed. As a result, we provide a trained model on a series of images that can be used for classifying images related to these two conditions. The performance of our neural network on the pre-trained images is 94%.