Impact of the data augmentation on the detection of brain tumor from MRI images based on CNN and pretrained models

Deep Learning has significantly push forward the research on cancer magnetic resonance imaging (MRI) images. These images are widely used to diagnose the presence of a deformed tissue within the brain in which the cells replicate indefinitely without control, i.e. a brain tumor. Radiologist have to deeply examine a set of MRI images for each patient in order to decide whether the tumor is benign (noncancerous) or malignant (cancerous). The latest have very severe consequences and have a very high mortality rate, but this could be significantly reduced if the cancer is diagnosed at an earlier stage. The classification task is very complicated due to neurological and radiological similarities of different tumors. In order to assist the radiologists, our objective in this paper is to achieve a correct classification of the MRI images. The studied deep classification models have been trained over three types of tumors: meningioma, glioma and pituitary tumor, on sagittal, coronal and axial views in addition to MRI of normal patients. The proposed model consists of combining a set of several classifiers that uses the features extracted by a convolutional neural network (CNN). We will, also, explore the impact of data augmentation and image resolution on the classification performance with the goal of obtaining the best possible accuracy. We used a CNN architecture and several pre-trained models. The model ResNet 18 gave the highest accuracy of 95.7%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.