mlsanityData to 14 Oct 2026

Brain Tumor Detection using Convolutional Neural Networks with Skip Connections

arXiv 2307.07503 ↗First posted 14 Jul 2023eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 14 Jul 2023.

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7 of 27 references.

References · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  3. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  4. 2014Deep Visual-Semantic Alignments for Generating Image Descriptionscited by 968
  5. 2018A Survey on Deep Transfer Learningcited by 419
  6. 2020A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospectscited by 397
  7. 2022Examining the behaviour of state-of-the-art convolutional neural networks for brain tumor detection with and without transfer learningcited by 1

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