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Transfer learning networks with skip connections for classification of brain tumors
Author(s) -
Alaraimi Saleh,
Okedu Kenneth E.,
Tianfield Hugo,
Holden Richard,
Uthmani Omair
Publication year - 2021
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22546
Subject(s) - transfer of learning , computer science , convolutional neural network , preprocessor , artificial intelligence , pattern recognition (psychology) , domain (mathematical analysis) , transfer (computing) , deep learning , clarity , machine learning , mathematics , mathematical analysis , biochemistry , chemistry , parallel computing
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.