Towards Radiologist-Level Accurate Deep Learning System for Pulmonary Screening
This work addresses the need for accurate, automated diagnosis tools in medical imaging, potentially aiding radiologists in screening for pneumonia and tuberculosis.
The authors tackled the problem of automated pulmonary disease screening from X-ray images, achieving state-of-the-art performance on multiple public datasets.
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.