A Lightweight Deep Learning Model and Web Interface for COVID-19 Detection Using Chest X-Rays
Bibliographic record
Abstract
COVID-19 is one of the deadly diseases that affected the global health system.It is difficult to diagnose COVID-19, as it shows the symptoms of the common cold.Therefore, effective screening techniques play a significant role in the timely detection of this disease.Existing techniques such as real-time reverse transcriptase-polymerase chain reaction (RT-PCR), require a considerable amount of time for processing, typically taking up to 48 hours to produce results.This delay can be detrimental, as the virus can spread rapidly during this waiting period.X-ray images are also used for this purpose due to their accessibility, speed, non-invasiveness, cost-effectiveness, ability to visualize lung tissues, and rapid deploy ability.This research proposes a convolutional neural network (CNN) to detect COIVD-19 based on chest X-ray images.The model's uniqueness lies in its ability to harness the power of convolutional layers for feature extraction without the need for complex segmentation techniques.The convolutional layers of the CNN filter slide across the input image, performing element-wise multiplication and accumulation to create feature maps.These maps highlight relevant patterns, edges, and textures present in the image.This can help in predicting the infection and its severity.With the proposed model an accuracy of 99% was achieved, and it attempts to balance computational efficiency and accuracy.Further, a web interface is developed so that users can use this model to obtain easy and accurate predictions.The proposed model aims to reduce the workload of healthcare workers and provide timely results to a patient so that further actions can be taken quickly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".