# Towards Radiologist-Level Accurate Deep Learning System for Pulmonary Screening

arXiv: https://arxiv.org/abs/1807.03120 · HTML: https://mlsanity.com/paper/1807.03120/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis](https://mlsanity.com/paper/2410.01216/) (2024)
- [Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images](https://mlsanity.com/paper/2501.16249/) (2025)
- [Monkeypox disease recognition model based on improved SE-InceptionV3](https://mlsanity.com/paper/2403.10087/) (2024)
- [Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images](https://mlsanity.com/paper/2409.11644/) (2024)

## Abstract (arXiv)

In this work, we propose advanced pneumonia and Tuberculosis grading system for X-ray images. The proposed system is a very deep fully convolutional classification network with online augmentation that outputs confidence values for diseases prevalence. Its a fully automated system capable of disease feature understanding without any offline preprocessing step or manual feature extraction. We have achieved state- of-the- art performance on the public databases such as ChestXray-14, Mendeley, Shenzhen Hospital X-ray and Belarus X-ray set.