| Autor: |
Ismail Mohd Khairuddin, Muhammad Amirul Abdullah, Anwar P. P. Abdul Majeed, W. H. Mohd Isa, Mohd Azraai Mohd Razman, Jessnor Arif Mat Jizat, A. H. M. Ismail |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Advances in Robotics, Automation and Data Analytics ISBN: 9783030709167 |
| DOI: |
10.1007/978-3-030-70917-4_36 |
| Popis: |
Transfer Learning (TL) opens new possibilities of detection of disease through radiography as compared to conventional machine learning as well as deep learning methods. The extraction of features through pre-trained Convolutional Neural Networks (CNN) and the tuning of the fully connected layers of the CNN model is the core for the development of a transfer learning pipeline. The present study investigates the diagnosis of COVID-19 through X-ray images by means of three TL models, namely Inception V3, VGG-16, and the VGG-19 for feature extraction along with heuristically fine-tuned fully connected layers. It was demonstrated through this preliminary work that both the VGG-16 and VGG-19 tuned pipelines could achieve a train and test classification accuracies of 99.8% and 94%, respectively. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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