Selected article for: "deep learning and detection performance"
Author: Rohila, Varan Singh; Gupta, Nitin; Kaul, Amit; Sharma, Deepak Kumar
Title: Deep Learning Assisted COVID-19 Detection using full CT-scans
  • Cord-id: ehzo685i
  • Document date: 2021_2_24
    • ID: ehzo685i
    Snippet: The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in automated diagnosis for speeding up the process while maintaining accuracy and reducing the computational requirements. This work proposes an automated diagnosis of COVID-19 infection from CT scans of the patients using deep learning technique. The proposed model, ReCOV-101, uses full chest CT scans to detect varying degrees of COVID-19 infection. To improve the detecti
    Document: The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in automated diagnosis for speeding up the process while maintaining accuracy and reducing the computational requirements. This work proposes an automated diagnosis of COVID-19 infection from CT scans of the patients using deep learning technique. The proposed model, ReCOV-101, uses full chest CT scans to detect varying degrees of COVID-19 infection. To improve the detection accuracy, the CT-scans were preprocessed by employing segmentation and interpolation. The proposed scheme is based on the residual network that takes advantage of skip connection, allowing the model to go deeper. The model was trained on a single enterprise-level GPU. It can easily be provided on a network’s edge, reducing communication with the cloud, often required for larger neural networks. This work aims to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can be combined with medical equipment and help ease the examination procedure. With the proposed model, an accuracy of 94.9% was achieved.

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