A mobile AI-enabled platform for screening human skin diseases

Abstract

Computer-aided systems for skin lesion diagnosis is a growing area of research. Researchers have recently shown an increasing interest in developing computer-aided diagnosis systems. This project aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. The experimentation is carried out on two publicly available datasets, Dermnet and Dermquest. The dataset is partitioned into training data and validation data for each category of skin disease. The train data consists of 80% of the dataset while the validation data consists of 20%. In the proposed pre-processing, we apply lesion segmentation and augmentation. This involves increasing the amount of data by adding slightly modified copies of already existing data (geometry- and intensity-based). The algorithm used for training is the sequential CNN model and the model is finally deployed on the developed Android application. The achieved accuracy on the training and validation is 87% and 85% without data augmentation. After augmenting the data, the accuracy of the training and validation data increases to 90.25% and 89.01% respectively. The trained model provides better classification outcomes on the different datasets, leading to a better recognition tool to assist dermatologists. With higher quality and a larger quantity of data, it will be viable to use state-of-the-art models to enable the use of CAD in the field of dermatology.