Spatial Pyramid Pooling in Deep Convolutional Networks for Automatic Tuberculosis Diagnosis
Why this work is in the frame
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Bibliographic record
Abstract
In recent decades, automatic diagnosis using machine-learning techniques have been the focus of research. Mycobacterium Tuberculosis (TB) is a deadly disease that has plagued most developing countries presents a problem that can be tackled by automatic diagnosis. The World Health Organization (WHO) set years 2030 and 2035 as milestones for a significant reduction in new infections and deaths although lack of well-trained professionals and insufficient or fragile public health systems (in developing countries) are just some of the major factors that have slowed the eradication of the TB endemic. Deep convolutional neural networks (DCNNs) have demonstrated remarkable results across problem domains dealing with grid-like data (i.e., images and videos). Traditionally, a methodology for detecting TB is through radiology combined with previous success DCNN have achieved in image classification makes them the perfect candidate to classify Chest X-Ray (CXR) images. In this study, we propose three types of DCNN trained using two public datasets and another new set which we collected from Konya Education and Research Hospital, Konya, Turkey. Also, the DCNN architectures were integrated with an extra layer called Spatial Pyramid Pooling (SPP) a methodology that equips convolutional neural networks with the ability for robust feature pooling by using spatial bins. The result indicates the potential for an automated system to diagnose tuberculosis with accuracies above a radiologist professional.
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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 | 0.000 | 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) | 0.001 | 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 it