Towards Radiologist-Level Accurate Deep Learning System for Pulmonary Screening
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Jun 2018.
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Citing papers per year: 2024: 3; 2025: 1.
4 papers.
- 2024RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosiscited by 4
- 2025Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Imagescited by 1
- 2024Monkeypox disease recognition model based on improved SE-InceptionV3cited by 0
- 2024Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Imagescited by 0
- 2025Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images27 Jan 2025
- 2024RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis2 Oct 2024
- 2024Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images18 Sep 2024
- 2024Monkeypox disease recognition model based on improved SE-InceptionV315 Mar 2024