Abstract:
Brain tumor recognition by magnetic resonance imaging (MRI) is crucial because it
improves survival rates and allows them to plan treatments accordingly. The patient is at
risk if a tumour in the brain, which is made up of a cluster of abnormal cells, spreads to
nearby tissues. MRI is the primary technique of imaging which is used for determining
the extent of brain tumours. Deep Learning techniques have rapidly expanded in
popularity in computer vision applications due to the abundance of data available for
training models and advancements in designing models that provide more accurate
estimations. When using deep learning techniques to recognize and categorize brain
tumors, magnetic resonance imaging (MRI) has produced satisfactory performance. In
this paper, we develop a strong deep-learning model which classifies brain tumors into
four groups depending on MRI scans using a CNN. Unsolicited areas of brain tumours
are deleted with the help of artefact removal, lowering noise, and quality-enhanced
images. With improved image quality the cancer is tinted. The number of MRI images
has increased using two augmentation techniques. The augmented dataset was analyzed
by a number of CNN architectures, including VGG19, MobileNetV2, InceptionV3,
VGG16, and Mobile Net. In this situation, VGG-16 offers the highest level of accuracy. The best model was then chosen, and a ablation study was performed on it based on the
hyperparameters. The best outcomes were achieved by the hyper-tuned VGG16, which
had test accuracy of 98.56% and validation and test accuracy of 99.23%.
