Brain Tumor Detection using Convolutional Neural Networks with Skip Connections

Aupam Hamran, Marzieh Vaeztourshizi, Amirhossein Esmaili, Massoud Pedram
7/14/2023
eess.IVcs.CVcs.LG

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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.

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Cite this paper

@article{hamran2023brain,
  title  = {Brain Tumor Detection using Convolutional Neural Networks with Skip Connections},
  author = {Aupam Hamran and Marzieh Vaeztourshizi and Amirhossein Esmaili and Massoud Pedram},
  year   = {2023},
  eprint = {2307.07503},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.07503v1}
}

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