COVID-19 Detection using Transfer Learning with Convolutional Neural Network
| Autor: | Dutta, Pramit, Roy, Tanny, Anjum, Nafisa |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), DHAKA, Bangladesh, 2021, pp. 429-432 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/ICREST51555.2021.9331029 |
| Popis: | The Novel Coronavirus disease 2019 (COVID-19) is a fatal infectious disease, first recognized in December 2019 in Wuhan, Hubei, China, and has gone on an epidemic situation. Under these circumstances, it became more important to detect COVID-19 in infected people. Nowadays, the testing kits are gradually lessening in number compared to the number of infected population. Under recent prevailing conditions, the diagnosis of lung disease by analyzing chest CT (Computed Tomography) images has become an important tool for both diagnosis and prophecy of COVID-19 patients. In this study, a Transfer learning strategy (CNN) for detecting COVID-19 infection from CT images has been proposed. In the proposed model, a multilayer Convolutional neural network (CNN) with Transfer learning model Inception V3 has been designed. Similar to CNN, it uses convolution and pooling to extract features, but this transfer learning model contains weights of dataset Imagenet. Thus it can detect features very effectively which gives it an upper hand for achieving better accuracy. Comment: 4 pages, 4 figures, 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), DHAKA, Bangladesh |
| Databáze: | arXiv |
| Externí odkaz: |
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