Chest X-Ray image and pathological data based artificial intelligence enabled dual diagnostic method for multi-stage classification of COVID-19 patients
The use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients wi...
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AIMS Press
2021-11-01
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| Series: | AIMS Biophysics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/biophy.2021028?viewType=HTML |
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| author | Swarnava Biswas Debajit Sen Dinesh Bhatia Pranjal Phukan Moumita Mukherjee |
| author_facet | Swarnava Biswas Debajit Sen Dinesh Bhatia Pranjal Phukan Moumita Mukherjee |
| author_sort | Swarnava Biswas |
| collection | DOAJ |
| description | The use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients with an annotated stage to enable the medical staff to manually activate a geo-fence around the subject thus ensuring early detection and isolation. The use of radiography images with pathology data used for COVID-19 identification forms the first-ever contribution by any research group globally. The novelty lies in the correct stage classification of COVID-19 subjects as well. The present analysis would bring this AI Model on the edge to make the facility an IoT-enabled unit. The developed system has been compared and extensively verified thoroughly with those of clinical observations. The significance of radiography imaging for detecting and identification of COVID-19 subjects with severity score tag for stage classification is mathematically established. In a Nutshell, this entire algorithmic workflow can be used not only for predictive analytics but also for prescriptive analytics to complete the entire pipeline from the diagnostic viewpoint of a doctor. As a matter of fact, the authors have used a supervised based learning approach aided by a multiple hypothesis based decision fusion based technique to increase the overall system's accuracy and prediction. The end to end value chain has been put under an IoT based ecosystem to leverage the combined power of AI and IoT to not only detect but also to isolate the coronavirus affected individuals. To emphasize further, the developed AI model predicts the respective categories of a coronavirus affected patients and the IoT system helps the point of care facilities to isolate and prescribe the need of hospitalization for the COVID patients. |
| first_indexed | 2024-04-14T03:35:53Z |
| format | Article |
| id | doaj.art-7f424e551e8d4a589b22fbfe8abf1b47 |
| institution | Directory Open Access Journal |
| issn | 2377-9098 |
| language | English |
| last_indexed | 2024-04-14T03:35:53Z |
| publishDate | 2021-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Biophysics |
| spelling | doaj.art-7f424e551e8d4a589b22fbfe8abf1b472022-12-22T02:14:45ZengAIMS PressAIMS Biophysics2377-90982021-11-018434637110.3934/biophy.2021028Chest X-Ray image and pathological data based artificial intelligence enabled dual diagnostic method for multi-stage classification of COVID-19 patientsSwarnava Biswas0Debajit Sen1Dinesh Bhatia2Pranjal Phukan3Moumita Mukherjee41. The Neotia University, Kolkata, West Bengal, India2. Robert Bosch Engineering and Business Solutions, Bangalore, Karnataka, India3. Department of Biomedical Engineering, North Eastern Hill University (NEHU), Shillong, Meghalaya, India4. Department of Radiology and Imaging, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, Meghalaya, India5. Department of Physics, School of Basic and Applied Sciences, Adamas University, Kolkata, West Bengal, IndiaThe use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients with an annotated stage to enable the medical staff to manually activate a geo-fence around the subject thus ensuring early detection and isolation. The use of radiography images with pathology data used for COVID-19 identification forms the first-ever contribution by any research group globally. The novelty lies in the correct stage classification of COVID-19 subjects as well. The present analysis would bring this AI Model on the edge to make the facility an IoT-enabled unit. The developed system has been compared and extensively verified thoroughly with those of clinical observations. The significance of radiography imaging for detecting and identification of COVID-19 subjects with severity score tag for stage classification is mathematically established. In a Nutshell, this entire algorithmic workflow can be used not only for predictive analytics but also for prescriptive analytics to complete the entire pipeline from the diagnostic viewpoint of a doctor. As a matter of fact, the authors have used a supervised based learning approach aided by a multiple hypothesis based decision fusion based technique to increase the overall system's accuracy and prediction. The end to end value chain has been put under an IoT based ecosystem to leverage the combined power of AI and IoT to not only detect but also to isolate the coronavirus affected individuals. To emphasize further, the developed AI model predicts the respective categories of a coronavirus affected patients and the IoT system helps the point of care facilities to isolate and prescribe the need of hospitalization for the COVID patients.https://www.aimspress.com/article/doi/10.3934/biophy.2021028?viewType=HTMLartificial intelligenceinternet of thingsdeep learningmachine learningfuzzy logiccovid-19 detectionx-raypathology datastage classification covid-19raspberry piintel®movidius™neural compute stick |
| spellingShingle | Swarnava Biswas Debajit Sen Dinesh Bhatia Pranjal Phukan Moumita Mukherjee Chest X-Ray image and pathological data based artificial intelligence enabled dual diagnostic method for multi-stage classification of COVID-19 patients AIMS Biophysics artificial intelligence internet of things deep learning machine learning fuzzy logic covid-19 detection x-ray pathology data stage classification covid-19 raspberry pi intel® movidius™ neural compute stick |
| title | Chest X-Ray image and pathological data based artificial intelligence enabled dual diagnostic method for multi-stage classification of COVID-19 patients |
| title_full | Chest X-Ray image and pathological data based artificial intelligence enabled dual diagnostic method for multi-stage classification of COVID-19 patients |
| title_fullStr | Chest X-Ray image and pathological data based artificial intelligence enabled dual diagnostic method for multi-stage classification of COVID-19 patients |
| title_full_unstemmed | Chest X-Ray image and pathological data based artificial intelligence enabled dual diagnostic method for multi-stage classification of COVID-19 patients |
| title_short | Chest X-Ray image and pathological data based artificial intelligence enabled dual diagnostic method for multi-stage classification of COVID-19 patients |
| title_sort | chest x ray image and pathological data based artificial intelligence enabled dual diagnostic method for multi stage classification of covid 19 patients |
| topic | artificial intelligence internet of things deep learning machine learning fuzzy logic covid-19 detection x-ray pathology data stage classification covid-19 raspberry pi intel® movidius™ neural compute stick |
| url | https://www.aimspress.com/article/doi/10.3934/biophy.2021028?viewType=HTML |
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