Selected article for: "CT volume and patient level"
Author: Ahmed, Sara Atito Ali; Yavuz, Mehmet Can; Sen, Mehmet Umut; Gulsen, Fatih; Tutar, Onur; Korkmazer, Bora; Samanci, Cesur; Sirolu, Sabri; Hamid, Rauf; Eryurekli, Ali Ergun; Mammadov, Toghrul; Yanikoglu, Berrin
Title: COVID-19 Detection in Computed Tomography Images with 2D and 3D Approaches
  • Cord-id: jejart3k
  • Document date: 2021_5_16
    • ID: jejart3k
    Snippet: Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the definitive RT-PCR test. We present a deep learning ensemble for detecting COVID-19 infection, combining slice-based (2D) and volume-based (3D) approaches. The 2D system detects the infection on each CT slice independently, combining them to obtain the patient-level decision via different methods (averaging and long-short term memory networks). The 3D system takes the whole CT volume to a
    Document: Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the definitive RT-PCR test. We present a deep learning ensemble for detecting COVID-19 infection, combining slice-based (2D) and volume-based (3D) approaches. The 2D system detects the infection on each CT slice independently, combining them to obtain the patient-level decision via different methods (averaging and long-short term memory networks). The 3D system takes the whole CT volume to arrive to the patient-level decision in one step. A new high resolution chest CT scan dataset, called the IST-C dataset, is also collected in this work. The proposed ensemble, called IST-CovNet, obtains 90.80% accuracy and 0.95 AUC score overall on the IST-C dataset in detecting COVID-19 among normal controls and other types of lung pathologies; and 93.69% accuracy and 0.99 AUC score on the publicly available MosMed dataset that consists of COVID-19 scans and normal controls only. The system is deployed at Istanbul University Cerrahpasa School of Medicine.

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