A deep convolution neural network for automated COVID-19 disease detection using chest X-ray images
COVID-19 is a virus that can cause severe pneumonia, and the severity varies based on the patient's immune system. The rapid spread of the disease can be mitigated through automated detection, addressing the shortage of radiologists in medicine. This paper introduces the Modified-Inception V3 (...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442523001454 |
| _version_ | 1797627742310629376 |
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| author | Rajasekaran Thangaraj Pandiyan P Jayabrabu Ramakrishnan Nallakumar R Sivaraman Eswaran |
| author_facet | Rajasekaran Thangaraj Pandiyan P Jayabrabu Ramakrishnan Nallakumar R Sivaraman Eswaran |
| author_sort | Rajasekaran Thangaraj |
| collection | DOAJ |
| description | COVID-19 is a virus that can cause severe pneumonia, and the severity varies based on the patient's immune system. The rapid spread of the disease can be mitigated through automated detection, addressing the shortage of radiologists in medicine. This paper introduces the Modified-Inception V3 (MIn-V3) model, which utilizes feature fusion from the internal layers of Inception V3 to classify different diseases, including normal cases, COVID-19 positivity, viral pneumonia, and bacterial pneumonia. Additionally, transfer learning and fine-tuning techniques are applied to enhance accuracy. The performance of MIn-V3 is assessed by comparing it with pre-trained Deep Learning (DL) models, such as Inception-ResNet V2 (InRN-V2), Inception V3, and MobileNet V2. Experimental results demonstrate that the MIn-V3 model surpasses other pre-trained models with a classification accuracy of 96.33 %. Furthermore, integrating the MIn-V3 model into a mobile application enables rapid and accurate detection of COVID-19, thus playing a crucial role in advancing early diagnostics, which is essential for timely intervention and effective disease management. |
| first_indexed | 2024-03-11T10:29:46Z |
| format | Article |
| id | doaj.art-788806f89b5448a2bf20e4eab28b6c37 |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-03-11T10:29:46Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-788806f89b5448a2bf20e4eab28b6c372023-11-15T04:12:16ZengElsevierHealthcare Analytics2772-44252023-12-014100278A deep convolution neural network for automated COVID-19 disease detection using chest X-ray imagesRajasekaran Thangaraj0Pandiyan P1Jayabrabu Ramakrishnan2Nallakumar R3Sivaraman Eswaran4KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, IndiaKPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, IndiaJazan University, Jazan, Saudi ArabiaKarpagam Institute of Technology, Coimbatore, Tamil Nadu, IndiaCurtin University, Miri, Sarawak, Malaysia; Corresponding author.COVID-19 is a virus that can cause severe pneumonia, and the severity varies based on the patient's immune system. The rapid spread of the disease can be mitigated through automated detection, addressing the shortage of radiologists in medicine. This paper introduces the Modified-Inception V3 (MIn-V3) model, which utilizes feature fusion from the internal layers of Inception V3 to classify different diseases, including normal cases, COVID-19 positivity, viral pneumonia, and bacterial pneumonia. Additionally, transfer learning and fine-tuning techniques are applied to enhance accuracy. The performance of MIn-V3 is assessed by comparing it with pre-trained Deep Learning (DL) models, such as Inception-ResNet V2 (InRN-V2), Inception V3, and MobileNet V2. Experimental results demonstrate that the MIn-V3 model surpasses other pre-trained models with a classification accuracy of 96.33 %. Furthermore, integrating the MIn-V3 model into a mobile application enables rapid and accurate detection of COVID-19, thus playing a crucial role in advancing early diagnostics, which is essential for timely intervention and effective disease management.http://www.sciencedirect.com/science/article/pii/S2772442523001454Deep convolution neural networkTransfer learningCOVID-19Feature fusionMulti-class classification |
| spellingShingle | Rajasekaran Thangaraj Pandiyan P Jayabrabu Ramakrishnan Nallakumar R Sivaraman Eswaran A deep convolution neural network for automated COVID-19 disease detection using chest X-ray images Healthcare Analytics Deep convolution neural network Transfer learning COVID-19 Feature fusion Multi-class classification |
| title | A deep convolution neural network for automated COVID-19 disease detection using chest X-ray images |
| title_full | A deep convolution neural network for automated COVID-19 disease detection using chest X-ray images |
| title_fullStr | A deep convolution neural network for automated COVID-19 disease detection using chest X-ray images |
| title_full_unstemmed | A deep convolution neural network for automated COVID-19 disease detection using chest X-ray images |
| title_short | A deep convolution neural network for automated COVID-19 disease detection using chest X-ray images |
| title_sort | deep convolution neural network for automated covid 19 disease detection using chest x ray images |
| topic | Deep convolution neural network Transfer learning COVID-19 Feature fusion Multi-class classification |
| url | http://www.sciencedirect.com/science/article/pii/S2772442523001454 |
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