SpiCoNET: A Hybrid Deep Learning Model to Diagnose COVID-19 and Pneumonia Using Chest X-Ray Images
Bibliographic record
Abstract
Using deep learning techniques on radiological lung images for detecting COVID-19 is a promising technique in shortening the diagnosis time. In this study, we propose a hybrid deep learning model, detecting the COVID-19 and Pneumonia virus using Chest X-ray images. The proposed model, named SpiCoNET, first runs multiple well-known deep learning models combined with Spiking Neural Network (SNN) in order to identify the models with higher accuracy rates. Then, SpiCoNET combines the features of the two models with the highest accuracy rates among the well-known models and hands the combined features over to a different SNN layer as an input. Finally, the features are classified by using the SEFRON learning algorithm. The proposed hybrid deep learning model takes advantage of the features of the well-known models combined with SNN providing the highest accuracy rate. Moreover, the proposed model makes use of the SEFRON learning algorithm to provide better classification. The proposed model provides an accuracy rate of 97.09% for the classification of images of the COVID-19, Pneumonia and Normal, which outperforms AlexNet (91.27%) and DenseNet201 (90.40%). The results reveal that deep learning based systems for the identification of COVID-19 and Pneumonia can help healthcare professionals control the COVID-19 pandemic in an effective manner.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".