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CovMUNET: A Multiple Loss Approach towards Detection of COVID-19 from Chest X-ray
The recent outbreak of COVID-19 has halted the whole world, bringing a d...
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COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest-Ray Images
Background and Objective: COVID-19 outbreak was declared as a pandemic o...
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CXR-Net: An Artificial Intelligence Pipeline for Quick Covid-19 Screening of Chest X-Rays
CXR-Net is a two-module Artificial Intelligence pipeline for the quick d...
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COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
The COVID-19 is estimated to have a high impact on the healthcare system...
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Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning
This paper introduces MuRAD (Musculoskeletal Radiograph Abnormality Dete...
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Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection
Osteoporotic vertebral fractures have a severe impact on patients' overa...
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PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention
Automatic Chest Radiograph X-ray (CXR) interpretation by machines is an ...
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FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection
To test the possibility of differentiating chest x-ray images of COVID-19 against other pneumonia and healthy patients using deep neural networks. We construct the X-ray imaging data from two publicly available sources, which include 5508 chest x-ray images across 2874 patients with four classes: normal, bacterial pneumonia, non-COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a Focal Loss Based Neural Ensemble Network (FLANNEL), a flexible module to ensemble several convolutional neural network (CNN) models and fuse with a focal loss for accurate COVID-19 detection on class imbalance data. FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics. Compared with the best baseline, FLANNEL shows a higher macro-F1 score with 6 identification task where it achieves 0.7833(0.07) in Precision, 0.8609(0.03) in Recall, and 0.8168(0.03) F1 score.
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