| Type of Material: | Thesis |
| Title: | Design and development of deep learning diagnostic model for biomedical applications of sars cov 2 |
| Researcher: | Deepanshi |
| Guide: | Budhiraja, Ishan | Garg, Deepak |
| Department: | School of Computer Science Engineering and Technology |
| Publisher: | Bennett University |
| Place: | Greater Noida |
| Year: | 2024 |
| Language: | English |
| Subject: | Computer Science | Computer Science Software Engineering | Engineering and Technology | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; School of Computer Science Engineering and Technology, Bennett University, Greater Noida; 2024 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-14 14:53:05 | |
| 008 | __ | 261014t2024||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464163 |
| 040 | __ | |dIN-AhILN|aBENN_201310 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aDeepanshi |
| 110 | __ | |aSchool of Computer Science Engineering and Technology|0U-0890|dGreater Noida|bBennett University|ein |
| 245 | __ | |aDesign and development of deep learning diagnostic model for biomedical applications of sars cov 2 |
| 260 | __ | |aGreater Noida|c2024|bBennett University |
| 300 | __ | |axv; 109p. |
| 502 | __ | |d2024|bPhD|cSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida |
| 518 | __ | |d2024|oDate of Award |
| 518 | __ | |d2020|oDate of Registration |
| 518 | __ | |d2024|oDate of Completion |
| 520 | __ | |aquotSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been the causative agent behind the global COVID-19 pandemic, imposing unprecedented challenges across mul tiple sectors, especially public health and medical research. Accurate and rapid diagnosis of this viral infection is paramount in controlling its spread and for the management of affected patients. The virus manifests in a variety of clinical symptoms ranging from mild to severe, making its diagnosis an intricate problem that requires high sensitivity and specificity. Traditional diagnostic methods such as Polymerase Chain Reaction (PCR) and serological tests have played a pivotal role in SARS-CoV-2 detection but suffer from several limitations includ ing prolonged turnaround times, variable sensitivity, and scalability issues. Deep Learning (DL) models have shown incredible promise in transforming healthcare diagnostics by automating and enhancing the analysis of complex biomedical data. This study is premised on the need to incorpo |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Software Engineering |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aBudhiraja, Ishan|eGuide |
| 700 | __ | |aGarg, Deepak|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/565472|yShodhganga |
| 905 | __ | |afromsg |
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