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Two-Stage Deep Learning Architecture For Pneumonia Detection And Its Diagnosis In Chest Radiographs, Barath Narayanan, Venkata Salini Priyamvada Davuluru, Russell C. Hardie
Two-Stage Deep Learning Architecture For Pneumonia Detection And Its Diagnosis In Chest Radiographs, Barath Narayanan, Venkata Salini Priyamvada Davuluru, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Approximately two million pediatric deaths occur every year due to Pneumonia. Detection and diagnosis of Pneumonia plays an important role in reducing these deaths. Chest radiography is one of the most commonly used modalities to detect pneumonia. In this paper, we propose a novel two-stage deep learning architecture to detect pneumonia and classify its type in chest radiographs. This architecture contains one network to classify images as either normal or pneumonic, and another deep learning network to classify the type as either bacterial or viral. In this paper, we study and compare the performance of various stage one networks such …
A Computationally Efficient U-Net Architecture For Lung Segmentation In Chest Radiographs, Barath Narayanan, Russell C. Hardie
A Computationally Efficient U-Net Architecture For Lung Segmentation In Chest Radiographs, Barath Narayanan, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Lung segmentation plays a crucial role in computer-aided diagnosis using Chest Radiographs (CRs). We implement a U-Net architecture for lung segmentation in CRs across multiple publicly available datasets. We utilize a private dataset with 160 CRs provided by the Riverain Medical Group for training purposes. A publicly available dataset provided by the Japanese Radiological Scientific Technology (JRST) is used for testing. The active shape model-based results would serve as the ground truth for both these datasets. In addition, we also study the performance of our algorithm on a publicly available Shenzhen dataset which contains 566 CRs with manually segmented lungs …