  Magnetic Resonance Imaging (MRI) is a principal diagnostic approach used in
the field of radiology to create images of the anatomical and physiological
structure of patients. MRI is the prevalent medical imaging practice to find
abnormalities in soft tissues. Traditionally they are analyzed by a radiologist
to detect abnormalities in soft tissues, especially the brain. The process of
interpreting a massive volume of patient's MRI is laborious. Hence, the use of
Machine Learning methodologies can aid in detecting abnormalities in soft
tissues with considerable accuracy. In this research, we have curated a novel
dataset and developed a framework that uses Deep Transfer Learning to perform a
multi-classification of tumors in the brain MRI images. In this paper, we
adopted the Deep Residual Convolutional Neural Network (ResNet50) architecture
for the experiments along with discriminative learning techniques to train the
model. Using the novel dataset and two publicly available MRI brain datasets,
this proposed approach attained a classification accuracy of 86.40% on the
curated dataset, 93.80% on the Harvard Whole Brain Atlas dataset, and 97.05%
accuracy on the School of Biomedical Engineering dataset. Results of our
experiments significantly demonstrate our proposed framework for transfer
learning is a potential and effective method for brain tumor
multi-classification tasks.
