Deep Learning for Grading Cardiomegaly Severity in Chest X-rays: An Investigation.
Candemir S, Rajaraman S, Thoma GR, Antani SK
Proc. IEEE Life Sciences Conference (LSC 2018), Montreal, Quebec, Canada, 28 – 30 October 2018. pp. 109-113.
Abstract:
This study investigates using deep convolutional neural networks (CNN) for automatic detection of cardiomegaly in digital chest X-rays (CXRs). First, we employ and finetune several deep CNN architectures to detect presence of cardiomegaly in CXRs. Next, we introduce a CXR-based pre-trained model where we first fully train an architecture with a very large CXR dataset and then fine-tune the system with cardiomegaly CXRs. Finally, we investigate the correlation between softmax probability of an architecture and the severity of the disease. We use two publicly available datasets, NLM-Indiana Collection and NIH-CXR datasets. Based on our preliminary results (i) data-driven approach produces better results than prior rulebased approaches developed for cardiomegaly detection, (ii) our preliminary experiment with alternative pre-trained model is promising, and (iii) the system is more confident if severity increases.
Candemir S, Rajaraman S, Thoma GR, Antani SK. Deep Learning for Grading Cardiomegaly Severity in Chest X-rays: An Investigation.
Proc. IEEE Life Sciences Conference (LSC 2018), Montreal, Quebec, Canada, 28 – 30 October 2018. pp. 109-113.