A Comparative Evaluation of Diverse Deep Learning Models for the COVID-19 Prediction
Bibliographic record
Abstract
Deep learning methodologies are now feasible in practically every sphere of modern life because to technological advancements. Because of its high level of accuracy, deep learning can automatically diagnose and classify a wide variety of medical conditions in the field of medicine. The coronavirus first appeared in Wuhan, China, in December 2019, and quickly spread throughout the world. The pandemic of COVID-19 presented significant challenges to the world's health care system. PCR and medical imaging can diagnose COVID-19. There has a negative impact on the health of people as well as the global economy, education, and social life. The most significant challenge in stymieing the rapid propagation of the disease is locating positive Corona patients as promptly as possible. Because there are no automated tool kits, additional diagnostic equipment will be required. According to radiological studies, these images include important information about the coronavirus. Accurate treatment of this virus and a solution to the problem of a lack of medical professionals in remote areas may be possible with the help of a specialized Artificial Intelligence (AI) system and radiographic pictures. We used pre-trained CNN models Xception, Inception, ResNet-50, ResNet-50V2, DenseNet121, and MobileNetV2 to correct the COVID-19 classification analytics. In this paper, we investigate COVID-19 detection methods that make use of chest X-rays. According to the findings of our research, the pre-trained CNN Model that makes use of MobileNetV2 performs better than other CNN techniques in terms of both the size of the solution and its speed. Our method might be of use to researchers in the process of fine-tuning the CNN model for efficient COVID screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".