  In this paper, we present different architectures of Convolutional Neural
Networks (CNN) to analyze and classify the brain tumors into benign and
malignant types using the Magnetic Resonance Imaging (MRI) technique. Different
CNN architecture optimization techniques such as widening and deepening of the
network and adding skip connections are applied to improve the accuracy of the
network. Results show that a subset of these techniques can judiciously be used
to outperform a baseline CNN model used for the same purpose.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning