Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks

@inproceedings{Lokwani2021AutomatedDO,
  title={Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks},
  author={Rohit Lokwani and Ashrika Gaikwad and V. Kulkarni and A. Pant and A. Kharat},
  booktitle={ICPRAM},
  year={2021}
}
COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003). Currently, swab samples are being used for its diagnosis. The most common testing method used is the RT-PCR method, which has high specificity but variable sensitivity. AI-based detection has the capability to overcome this drawback. In this paper, we propose a prospective method wherein we use chest CT scans to diagnose the patients for COVID-19 pneumonia. We use a set of open-source… Expand
Efficient Deep Network Architecture for COVID-19 Detection Using Computed Tomography Images
TLDR
A new framework to exploit powerful features extracted from the autoencoder and Gray Level Co-occurence Matrix (GLCM) combined with random forest algorithm for the efficient and fast detection of COVID-19 using computed tomographic images is proposed. Expand
A stacked ensemble for the detection of COVID-19 with high recall and accuracy
TLDR
A novel stacked ensemble capable of detecting COVID-19 from a patient’s chest CT scans with high recall and accuracy is proposed and the trade-offs between recall and precision were explored. Expand
Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review
TLDR
A systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019 to assist healthcare providers, guideline developers, and policymakers. Expand
Applications of artificial intelligence in battling against covid-19: A literature review
TLDR
An overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. Expand
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
TLDR
This paper presents a review of these recently emerging automatic diagnosing models and pointed out that domain adaption in transfer learning and interpretability promotion would be the possible future directions. Expand

References

SHOWING 1-10 OF 41 REFERENCES
Deep learning system to screen coronavirus disease 2019 pneumonia
TLDR
A study that compared multiple convolutional neural network models to classify CT samples with COVID-19, Influenza viral pneumonia, or no-infection, and achieved an AUC of 0.996 (95%CI: 0.989–1.00) for Coronavirus vs Non-coronav virus cases per thoracic CT studies is technically reviewed. Expand
Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.
  • Lin Li, Lixin Qin, +15 authors Jun Xia
  • Medicine
  • 2020
TLDR
A deep learning model was developed to extract visual features from volumetric chest CT scans for the detection of coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. Expand
Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
TLDR
A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases. Expand
A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)
TLDR
The results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Expand
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images
TLDR
A deep learning-based CT diagnosis system (DeepPneumonia) was developed and showed that the established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients. Expand
COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection
TLDR
The initial experimental results show that the model developed here can reliably detect 96.00% COVID-19 cases and 70.65% non-COVID- 19 cases when evaluated on 1531 Xray images with two splits of the dataset, offering a promise of the proposed model for reliable CO VID-19 screening of chest X-ray images. Expand
Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study
TLDR
The deep learning model showed a comparable performance with expert radiologist, and greatly improve the efficiency of radiologists in clinical practice, holds great potential to relieve the pressure of frontline radiologists, improve early diagnosis, isolation and treatment, and thus contribute to the control of the epidemic. Expand
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
TLDR
An algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists is developed, and it is found that CheXNet exceeds average radiologist performance on the F1 metric. Expand
Chest CT Findings in Cases from the Cruise Ship Diamond Princess with Coronavirus Disease (COVID-19)
TLDR
A high incidence of subclinical CT changes in cases with COVID-19 is documented, with asymptomatic cases showing more GGO over consolidation and milder extension of disease on CT compared with symptomatic cases. Expand
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
TLDR
A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset, and demonstrated the potential of CNNs in analyzing lung patterns. Expand
...
1
2
3
4
5
...