A Comparative Analysis of Data Augmentation Approaches for Magnetic Resonance Imaging (MRI) Scan Images of Brain Tumor

INTRODUCTION: Machine Learning (ML) is a rapidly growing subfield of Artificial Intelligence (AI). It is used for different purposes in our daily life such as face recognition, speech recognition, text translation in different languages, weather prediction, and business prediction. In parallel, ML also plays an important role in the medical domain such as in medical imaging. ML has various algorithms that need to be trained with large volumes of data to produce a well-trained model for prediction. AIM: The aim of this study is to highlight the most suitable Data Augmentation (DA) technique(s) for medical imaging based on their results. METHODS: DA refers to different approaches that are used to increase the size of datasets. In this study, eight DA approaches were used on publicly available low-grade glioma tumor datasets obtained from the Tumor Cancer Imaging Archive (TCIA) repository. The dataset included 1961 MRI brain scan images of low-grade glioma patients. You Only Look Once (YOLO) version 3 model was trained on the original dataset and the augmented datasets separately. A neural network training/testing ecosystem named as supervisely with Tesla K80 GPU was used for YOLO v3 model training on all datasets. RESULTS: The results showed that the DA techniques rotate at 180o and rotate at 90o performed the best as data enhancement techniques for medical imaging. CONCLUSION: Rotation techniques are found significant to enhance the low volume of medical imaging datasets.