55 papers with code • 1 benchmarks • 10 datasets
Covid-19 Diagnosis is the task of diagnosing the presence of COVID-19 in an individual with machine learning.
In late 2019 and after COVID-19 pandemic in the world, many researchers and scholars have tried to provide methods for detection of COVID-19 cases.
The AI model achieves COVID-19 sensitivity of 89. 5% +\- 0. 11, CAP sensitivity of 95% +\- 0. 11, normal cases sensitivity (specificity) of 85. 7% +\- 0. 16, and accuracy of 90% +\- 0. 06.
We propose a two-stage Convolutional Neural Network (CNN) based classification framework for detecting COVID-19 and Community-Acquired Pneumonia (CAP) using the chest Computed Tomography (CT) scan images.
We present our findings using publicly available chest pathologies (CheXpert, NIH ChestX-ray8) and COVID-19 datasets (BrixIA, and COVID-19 chest X-ray segmentation dataset).
We present a model that fuses instance segmentation, Long Short-Term Memory Network and Attention mechanism to predict COVID-19 and segment chest CT scans.
The novelty of the methodology is based on the computation of the affinity between the lesion masks’ features extracted from the image.
With COVID-19 cases rising rapidly, deep learning has emerged as a promising diagnosis technique.
We introduce a lightweight model based on Mask R-CNN with ResNet18 and ResNet34 backbone models that segments lesions and predicts COVID-19 from chest CT scans in a single shot.
Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray.