# Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation

arXiv: https://arxiv.org/abs/1803.01199 · HTML: https://mlsanity.com/paper/1803.01199/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2018.

## Built on by (most cited first, 24 of 24)

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- [Chest X-Ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings](https://mlsanity.com/paper/2104.04518/) (2021)
- [Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation](https://mlsanity.com/paper/2104.01975/) (2021)
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- [Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study](https://mlsanity.com/paper/2211.02475/) (2022)
- [Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?](https://mlsanity.com/paper/2301.04032/) (2023)
- [A comparative analysis of deep learning models for lung segmentation on X-ray images](https://mlsanity.com/paper/2404.06455/) (2024)
- [Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema](https://mlsanity.com/paper/2409.09677/) (2024)
- [Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks](https://mlsanity.com/paper/2410.00129/) (2024)
- [Uncertainty-Aware Learning Policy for Reliable Pulmonary Nodule Detection on Chest X-Ray](https://mlsanity.com/paper/2508.13236/) (2025)
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## Abstract (arXiv)

The results of chest X-ray (CXR) analysis of 2D images to get the statistically reliable predictions (availability of tuberculosis) by computer-aided diagnosis (CADx) on the basis of deep learning are presented. They demonstrate the efficiency of lung segmentation, lossless and lossy data augmentation for CADx of tuberculosis by deep convolutional neural network (CNN) applied to the small and not well-balanced dataset even. CNN demonstrates ability to train (despite overfitting) on the pre-processed dataset obtained after lung segmentation in contrast to the original not-segmented dataset. Lossless data augmentation of the segmented dataset leads to the lowest validation loss (without overfitting) and nearly the same accuracy (within the limits of standard deviation) in comparison to the original and other pre-processed datasets after lossy data augmentation. The additional limited lossy data augmentation results in the lower validation loss, but with a decrease of the validation accuracy. In conclusion, besides the more complex deep CNNs and bigger datasets, the better progress of CADx for the small and not well-balanced datasets even could be obtained by better segmentation, data augmentation, dataset stratification, and exclusion of non-evident outliers.