Transfer learning networks with skip connections for classification of brain tumors

Saleh Alaraimi*, Kenneth E. Okedu*, Hugo Tianfield, Richard Holden, Omair Uthmani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
208 Downloads (Pure)


This article presents a transfer learning model via convolutional neural networks (CNNs) with skip connection topology, to avoid the vanishing gradient and time complexity, which are usually common in transfer learning networks. Three pretrained CNN architectures, namely AlexNet, VGG16 and GoogLeNet are employed to equip with skip connections. The transfer learning is implemented through fine‐tuning and freezing the CNN architectures with skip connections based on magnetic resonance imaging (MRI) slices of brain tumor dataset. Furthermore, in the preprocessing, a frequency‐domain information enhancement technique is employed for better image clarity. Performance evaluation is conducted on the transfer learning networks with skip connections to obtain improved accuracy in brain MRI classifications.
Original languageEnglish
Pages (from-to)1564-1582
Number of pages19
JournalInternational Journal of Imaging Systems and Technology
Issue number3
Early online date4 Feb 2021
Publication statusPublished - Sept 2021


  • AlexNet
  • convolutional neural network (CNN)
  • deep learning
  • GoogLeNet
  • transfer learning
  • VGG

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Software
  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition


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