Version 1
: Received: 26 July 2021 / Approved: 28 July 2021 / Online: 28 July 2021 (17:12:31 CEST)
How to cite:
Kora Venu, S. Improving the generalization of Deep Learning Classification Models in Medical Imaging using Transfer Learning and Generative Adversarial Networks. Preprints2021, 2021070636. https://doi.org/10.20944/preprints202107.0636.v1
Kora Venu, S. Improving the generalization of Deep Learning Classification Models in Medical Imaging using Transfer Learning and Generative Adversarial Networks. Preprints 2021, 2021070636. https://doi.org/10.20944/preprints202107.0636.v1
Kora Venu, S. Improving the generalization of Deep Learning Classification Models in Medical Imaging using Transfer Learning and Generative Adversarial Networks. Preprints2021, 2021070636. https://doi.org/10.20944/preprints202107.0636.v1
APA Style
Kora Venu, S. (2021). Improving the generalization of Deep Learning Classification Models in Medical Imaging using Transfer Learning and Generative Adversarial Networks. Preprints. https://doi.org/10.20944/preprints202107.0636.v1
Chicago/Turabian Style
Kora Venu, S. 2021 "Improving the generalization of Deep Learning Classification Models in Medical Imaging using Transfer Learning and Generative Adversarial Networks" Preprints. https://doi.org/10.20944/preprints202107.0636.v1
Abstract
Data sets for medical images are generally imbalanced and limited in sample size because of high data collection costs, time-consuming annotations, and patient privacy concerns. The training of deep neural network classification models on these data sets to improve the generalization ability does not produce the desired results for classifying the medical condition accurately and often overfit the data on the majority of class samples. To address the issue, we propose a framework for improving the classification performance metrics of deep neural network classification models using transfer learning: pre-trained models, such as Xception, InceptionResNet, DenseNet along with the Generative Adversarial Network (GAN) – based data augmentation. Then, we trained the network by combining traditional data augmentation techniques, such as randomly flipping the image left to right and GAN-based data augmentation, and then fine-tuned the hyper-parameters of the transfer learning models, such as the learning rate, batch size, and the number of epochs. With these configurations, the Xception model outperformed all other pre-trained models achieving a test accuracy of 98.7%, the precision of 99%, recall of 99.3%, f1-score of 99.1%, receiver operating characteristic (ROC) - area under the curve (AUC) of 98.2%.
Keywords
Generative Adversarial Networks; Transfer Learning; Medical Imaging; Deep Learning Classification; Chest X-ray’s
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
