Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm
Open Access
- 7 November 2020
- journal article
- research article
- Published by MDPI AG in Journal of Personalized Medicine
- Vol. 10 (4), 213
- https://doi.org/10.3390/jpm10040213
Abstract
According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.This publication has 26 references indexed in Scilit:
- Deep learning in chest radiography: Detection of findings and presence of changePLOS ONE, 2018
- Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- A survey on deep learning in medical image analysisMedical Image Analysis, 2017
- Deep Learning in Medical Image AnalysisAnnual Review of Biomedical Engineering, 2017
- Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?IEEE Transactions on Medical Imaging, 2016
- Prediction models need appropriate internal, internal–external, and external validationJournal of Clinical Epidemiology, 2015
- A combined approach for the binarization of handwritten document imagesPattern Recognition Letters, 2014
- A Survey on Transfer LearningIEEE Transactions on Knowledge and Data Engineering, 2009
- Adaptive image contrast enhancement using generalizations of histogram equalizationIEEE Transactions on Image Processing, 2000
- Cross‐Validatory Choice and Assessment of Statistical PredictionsJournal of the Royal Statistical Society: Series B (Methodological), 1974