Hybrid CNN Architectures for Detection of Respiratory and Cardiac Diseases from Chest X-Ray Image Classification using Modern Approaches
Bibliographic record
Abstract
In the present era, the advancement in the field of medicine is rapidly transforming healthcare in the diagnosis and detection of diseases at the early stages. Respiratory diseases and cardiac diseases are the two major categories of diseases that are very common these days. As the number of cases is increasing the physician, scientists who specialize in artificial intelligence have to develop some deep learning model that will help in detection of the disease using the x ray of the chest of the infected persons. In this paper we had proposed some Convolutional Neural Network model to identify the pneumonia (respiratory cases) as well as cardiac arrest (cardiovascular cases). The model architecture is based on some traditional Convolutional Neural Network models such as VGG, Alex Net, Inception, ResNet as well as some modern Convolutional Neural Network model such as Incetion V3, ResNet 50, VGG 16, VGG 19, DenseNet 201, MobileNet, Xception. In this model we had taken a combination of nearly 3300 chest X-Ray images out of which 1000 are normal and 600 are of infected chest X-Ray images. Also, a comparative study has been performed on these approaches on the basis of some of the factors such as data size, epochs, optimizer used, random state, classifier used, and best performance is obtained by DenseNet201 and its accuracy is 99.9% with 25 epochs.This study will help the researchers to develop some more effective Convolutional Neural Network model to detect the cardiovascular and respiratory diseases.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".