Brain Tumor Detection using Convolutional Neural Networks with Skip Connections
This work addresses brain tumor detection for medical diagnosis, but it is incremental as it applies known CNN optimization techniques to a specific dataset.
The paper tackled brain tumor classification from MRI scans using various CNN architectures with skip connections, achieving improved accuracy over a baseline model.
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.