Utilizing CNN-LSTM techniques for the enhancement of medical systems
COVID-19 is one of the most chronic and serious infections of recent years due to its worldwide spread. Determining who was genuinely affected when the disease spreads more widely is challenging. More than 60% of affected individuals report having a dry cough. In many recent studies, diagnostic mode...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682300282X |
| _version_ | 1827966515064864768 |
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| author | Alanazi Rayan Sager holyl alruwaili Alaa S. Alaerjan Saad Alanazi Ahmed I. Taloba Osama R. Shahin Mostafa Salem |
| author_facet | Alanazi Rayan Sager holyl alruwaili Alaa S. Alaerjan Saad Alanazi Ahmed I. Taloba Osama R. Shahin Mostafa Salem |
| author_sort | Alanazi Rayan |
| collection | DOAJ |
| description | COVID-19 is one of the most chronic and serious infections of recent years due to its worldwide spread. Determining who was genuinely affected when the disease spreads more widely is challenging. More than 60% of affected individuals report having a dry cough. In many recent studies, diagnostic models were developed using coughing and other breathing sounds. With the development of technology, body sounds are now collected using digital techniques for respiratory and cardiovascular tests. Early research on identifying COVID-19 utilizing speech and diagnosing signs yielded encouraging findings. The gathering of extensive, multi-group, airborne acoustical sound data is used in the developed framework to conduct an efficient assessment to test for COVID-19. An effective classification model is created to assess COVID-19 utilizing deep learning methods. The MIT-Covid-19 dataset is used as the input, and the Weiner filter is used for pre-processing. Following feature extraction done by Mel-frequency cepstral coefficients, the classification is performed using the CNN-LSTM approach. The study compared the performance of the developed framework with other techniques such as CNN, GRU, and LSTM. Study results revealed that CNN-LSTM outperformed other existing approaches by 97.7%. |
| first_indexed | 2024-04-09T17:51:09Z |
| format | Article |
| id | doaj.art-224860ff137e4429bb6b53f9dd88297f |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-09T17:51:09Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-224860ff137e4429bb6b53f9dd88297f2023-04-16T04:11:41ZengElsevierAlexandria Engineering Journal1110-01682023-06-0172323338Utilizing CNN-LSTM techniques for the enhancement of medical systemsAlanazi Rayan0Sager holyl alruwaili1Alaa S. Alaerjan2Saad Alanazi3Ahmed I. Taloba4Osama R. Shahin5Mostafa Salem6Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakakah, Saudi Arabia; Corresponding author at: Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakakah, Saudi Arabia.Department of Surgery, Orthopedic Division, College of Medicine, Jouf University, Sakaka, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaDepartment of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakakah, Saudi ArabiaDepartment of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakakah, Saudi ArabiaDepartment of Computer Science, Faculty of Computers and Information, Assiut University, Assiut, EgyptCOVID-19 is one of the most chronic and serious infections of recent years due to its worldwide spread. Determining who was genuinely affected when the disease spreads more widely is challenging. More than 60% of affected individuals report having a dry cough. In many recent studies, diagnostic models were developed using coughing and other breathing sounds. With the development of technology, body sounds are now collected using digital techniques for respiratory and cardiovascular tests. Early research on identifying COVID-19 utilizing speech and diagnosing signs yielded encouraging findings. The gathering of extensive, multi-group, airborne acoustical sound data is used in the developed framework to conduct an efficient assessment to test for COVID-19. An effective classification model is created to assess COVID-19 utilizing deep learning methods. The MIT-Covid-19 dataset is used as the input, and the Weiner filter is used for pre-processing. Following feature extraction done by Mel-frequency cepstral coefficients, the classification is performed using the CNN-LSTM approach. The study compared the performance of the developed framework with other techniques such as CNN, GRU, and LSTM. Study results revealed that CNN-LSTM outperformed other existing approaches by 97.7%.http://www.sciencedirect.com/science/article/pii/S111001682300282XCovid-19Early diagnosisCNN-LSTMCough dataWeiner filterAutomatic detection |
| spellingShingle | Alanazi Rayan Sager holyl alruwaili Alaa S. Alaerjan Saad Alanazi Ahmed I. Taloba Osama R. Shahin Mostafa Salem Utilizing CNN-LSTM techniques for the enhancement of medical systems Alexandria Engineering Journal Covid-19 Early diagnosis CNN-LSTM Cough data Weiner filter Automatic detection |
| title | Utilizing CNN-LSTM techniques for the enhancement of medical systems |
| title_full | Utilizing CNN-LSTM techniques for the enhancement of medical systems |
| title_fullStr | Utilizing CNN-LSTM techniques for the enhancement of medical systems |
| title_full_unstemmed | Utilizing CNN-LSTM techniques for the enhancement of medical systems |
| title_short | Utilizing CNN-LSTM techniques for the enhancement of medical systems |
| title_sort | utilizing cnn lstm techniques for the enhancement of medical systems |
| topic | Covid-19 Early diagnosis CNN-LSTM Cough data Weiner filter Automatic detection |
| url | http://www.sciencedirect.com/science/article/pii/S111001682300282X |
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