Brain Tumor Detection using Convolutional Neural Networks with Skip Connections

TL;DR

This paper explores various CNN architectures with skip connections and optimization techniques to improve brain tumor classification accuracy from MRI images.

Contribution

It introduces optimized CNN architectures with skip connections for more accurate brain tumor detection from MRI scans.

Findings

Skip connections improve CNN accuracy.

Optimized CNN outperforms baseline models.

Network widening and deepening enhance performance.

Abstract

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.