A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images
COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a s...
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Hindawi Limited
2021
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oai:doaj.org-article:6e4777ba1a534357aaa2a78c57513fc32021-11-15T01:18:55ZA Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images2040-230910.1155/2021/9437538https://doaj.org/article/6e4777ba1a534357aaa2a78c57513fc32021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9437538https://doaj.org/toc/2040-2309COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model. In deep learning, we have multiple approaches for building a classification system for analyzing radiographic images. In this work, we used the transfer learning technique. This approach makes it possible to store and use the knowledge acquired from a pretrained convolutional neural network to solve a new problem. To ensure the robustness of the proposed system for diagnosing patients with COVID-19 using X-ray images, we used a machine learning method called the stacking approach to combine the performances of the many transfer learning-based models. The generated model was trained on a dataset containing four classes, namely, COVID-19, tuberculosis, viral pneumonia, and normal cases. The dataset used was collected from a six-source dataset of X-ray images. To evaluate the performance of the proposed system, we used different common evaluation measures. Our proposed system achieves an extremely good accuracy of 99.23% exceeding many previous related studies.Soufiane HamidaOussama El GannourBouchaib CherradiAbdelhadi RaihaniHicham MoujahidHassan OuajjiHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021) |
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Medicine (General) R5-920 Medical technology R855-855.5 |
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Medicine (General) R5-920 Medical technology R855-855.5 Soufiane Hamida Oussama El Gannour Bouchaib Cherradi Abdelhadi Raihani Hicham Moujahid Hassan Ouajji A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images |
| description |
COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model. In deep learning, we have multiple approaches for building a classification system for analyzing radiographic images. In this work, we used the transfer learning technique. This approach makes it possible to store and use the knowledge acquired from a pretrained convolutional neural network to solve a new problem. To ensure the robustness of the proposed system for diagnosing patients with COVID-19 using X-ray images, we used a machine learning method called the stacking approach to combine the performances of the many transfer learning-based models. The generated model was trained on a dataset containing four classes, namely, COVID-19, tuberculosis, viral pneumonia, and normal cases. The dataset used was collected from a six-source dataset of X-ray images. To evaluate the performance of the proposed system, we used different common evaluation measures. Our proposed system achieves an extremely good accuracy of 99.23% exceeding many previous related studies. |
| format |
article |
| author |
Soufiane Hamida Oussama El Gannour Bouchaib Cherradi Abdelhadi Raihani Hicham Moujahid Hassan Ouajji |
| author_facet |
Soufiane Hamida Oussama El Gannour Bouchaib Cherradi Abdelhadi Raihani Hicham Moujahid Hassan Ouajji |
| author_sort |
Soufiane Hamida |
| title |
A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images |
| title_short |
A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images |
| title_full |
A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images |
| title_fullStr |
A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images |
| title_full_unstemmed |
A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images |
| title_sort |
novel covid-19 diagnosis support system using the stacking approach and transfer learning technique on chest x-ray images |
| publisher |
Hindawi Limited |
| publishDate |
2021 |
| url |
https://doaj.org/article/6e4777ba1a534357aaa2a78c57513fc3 |
| work_keys_str_mv |
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