Transfer learning networks with skip connections for classification of brain tumors. Issue 3 (4th February 2021)

• Record Type: Journal Article
• Authors: Alaraimi, Saleh; Okedu, Kenneth E.; Tianfield, Hugo; Holden, Richard; Uthmani, Omair
• Is Part Of: International journal of imaging systems and technology. Volume 31:Issue 3(2021)
• Page Start: 1564
• Page End: 1582
• Publication Date: 2021-02-04
• Subjects: AlexNet -- convolutional neural network (CNN) -- deep learning -- GoogLeNet -- transfer learning -- VGG; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22546
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: 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.