Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images
| العنوان: | Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images |
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| المؤلفون: | Ashish Rana, Harpreet Singh, Ravimohan Mavuduru, Smita Pattanaik, Prashant Singh Rana |
| المصدر: | Multimedia Tools and Applications. 81:18129-18153 |
| بيانات النشر: | Springer Science and Business Media LLC, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Computer Networks and Communications, Hardware and Architecture, Media Technology, Software |
| الوصف: | The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimationprogression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score. |
| تدمد: | 1573-7721 1380-7501 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3d3ead4c548bfad0117cf8f7cba4026Test https://doi.org/10.1007/s11042-022-12214-6Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....c3d3ead4c548bfad0117cf8f7cba4026 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15737721 13807501 |
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