Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study
Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pre-trained models have shown an effective perform...
Main Authors: | Laith Alzubaidi, Ye Duan, Ayad Al-Dujaili, Ibraheem Kasim Ibraheem, Ahmed H. Alkenani, Jose Santamaría, Mohammed A. Fadhel, Omran Al-Shamma, Jinglan Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
PeerJ Inc.
2021-09-01
|
Series: | PeerJ Computer Science |
Subjects: | |
Online Access: | https://peerj.com/articles/cs-715.pdf |
Similar Items
-
Radio–Image Transformer: Bridging Radio Modulation Classification and ImageNet Classification
by: Shichuan Chen, et al.
Published: (2020-10-01) -
On the Scale Invariance in State of the Art CNNs Trained on ImageNet
by: Mara Graziani, et al.
Published: (2021-04-01) -
Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data
by: Laith Alzubaidi, et al.
Published: (2021-03-01) -
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
by: Laith Alzubaidi, et al.
Published: (2021-03-01) -
Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis
by: Laith Alzubaidi, et al.
Published: (2020-03-01)