A COMPARATIVE ANALYSIS OF DATA AUGMENTATION APPROACHES FOR MRI SCAN IMAGES OF BRAIN TUMOR

Abstract

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 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. Training of such algorithms with small datasets is a challenging task. Data Augmentation (DA) refers to different approaches that are used to increase the size of datasets. In this study, eight DA approaches were used on medical images including; image rotation at 180o, rotation at 90o, noise, crop & scale, vertical flip, horizontal flip, shear and Gaussian blur. We used a publicly available low-grade glioma tumor dataset obtained from Tumor Cancer Imaging Archive (TCIA) repository. The dataset included 1961 MRI brain scan images of low-grade glioma patients. Each DA approach was applied on original images in the dataset to create new datasets by merging the results with the original dataset. You Only Look Once (YOLO) version 3 model was trained on original dataset and the augmented datasets separately. Supervisely with Tesla K80 GPU was used for YOLO v3 model training on all datasets. The results showed that the DA techniques rotate at 180o and rotate at 90o increased the accuracy of the results as 96% and 92% respectively. Crop & Scale achieved 83% accuracy. Rest of techniques achieved <80% accuracy. Rotate at 180o and rotate at 90o are found as the best DA approaches for augmenting low-grade glioma tumor medical imaging datasets.