Comparison of CNN Architectures for Mycobacterium Tuberculosis Classification in Sputum Images
Bibliographic record
Abstract
Tuberculosis (TB) is a preventable and treatable infectious disease, but remains a serious problem in high-risk countries.Accurate early detection remains a challenge despite prevention efforts.The primary method of detecting tuberculosis is identifying bacteria in sputum samples using a microscope.This research focuses on the use of Convolutional Neural Network (CNN) with the AlexNet, ResNet-18, ResNet-50, and VGG-16 architectures in the early detection and classification of Tuberculosis (TB) through processing images of TB patients' sputum.A dataset of sputum images was collected and processed to ensure quality and adequate representation.Each CNN model was trained using deep learning techniques on the prepared dataset.The aim of this research is to compare the performance of each model in recognizing and classifying sputum images containing Mycobacterium tuberculosis bacteria and those without TB bacteria.The research results show that AlexNet architecture outperforms ResNet-18, ResNet-50 and VGG-16 in classification accuracy of Mycobacterium tuberculosis.The best validation accuracy achieved was 93.42% with the fastest time of 5 minutes and 52 seconds using AlexNet architecture.Identifying the most appropriate AlexNet architectural model could unlock the potential for developing automated systems that efficiently identify TB, thereby enabling faster and more timely medical intervention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".