  Image processing concepts can visualize the different anatomy structure of
the human body. Recent advancements in the field of deep learning have made it
possible to detect the growth of cancerous tissue just by a patient's brain
Magnetic Resonance Imaging (MRI) scans. These methods require very high
accuracy and meager false negative rates to be of any practical use. This paper
presents a Convolutional Neural Network (CNN) based transfer learning approach
to classify the brain MRI scans into two classes using three pre-trained
models. The performances of these models are compared with each other.
Experimental results show that the Resnet-50 model achieves the highest
accuracy and least false negative rates as 95% and zero respectively. It is
followed by VGG-16 and Inception-V3 model with an accuracy of 90% and 55%
respectively.
