Deep Reinforcement Learning-based Automatic Augmentation for Gastrointestinal Disease Classification

Artificial Intelligence and Machine Learning have brought transformative changes to clinical diagnostics, especially in image classification via deep convolutional neural networks. The latter are crucial in analyzing and identifying diseases from medical visuals, ensuring accurate diagnosis and prevention. However, the limited availability of medical imaging data poses a serious challenge and leads to poor performance of the classifier. To address this issue, both geometric/color augmentation and synthetic data generation (through Generative Adversarial Networks and Variational Autoencoders) methods are employed, both operating on the entire dataset without an intrinsic optimization of the classification process. This study introduces an automated data augmentation strategy in which the editing operation is customized with respect to the individual images contained in the training set. This is done by using the Deep Reinforcement Learning framework provided by the Proximal Policy Optimization algorithm, with the reward being the test accuracy of the image classifier. The application of the proposed procedure on a meager dataset related to gastrointestinal diseases demonstrates an improvement in image classification by over 3%.