Author: Qiblawey, Yazan; Tahir, Anas; Chowdhury, Muhammad E. H.; Khandakar, Amith; Kiranyaz, Serkan; Rahman, Tawsifur; Ibtehaz, Nabil; Mahmud, Sakib; Al-Madeed, Somaya; Musharavati, Farayi
Title: Detection and severity classification of COVID-19 in CT images using deep learning Cord-id: mk3v6vav Document date: 2021_2_15
ID: mk3v6vav
Snippet: Since the breakout of coronavirus disease (COVID-19), the computer-aided diagnosis has become a necessity to prevent the spread of the virus. Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography (CT) images Furthermore, the system classifies the severity of COVID-19 as mild, moderate, severe, or critical based on
Document: Since the breakout of coronavirus disease (COVID-19), the computer-aided diagnosis has become a necessity to prevent the spread of the virus. Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography (CT) images Furthermore, the system classifies the severity of COVID-19 as mild, moderate, severe, or critical based on the percentage of infected lungs. An extensive set of experiments were performed using state-of-the-art deep Encoder-Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments showed the best performance for lung region segmentation with Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN model with the DenseNet201 encoder. The achieved performance is significantly superior to previous methods for COVID-19 lesion localization. Besides, the proposed system can reliably localize infection of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1,110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical infections, respectively.
Search related documents:
Co phrase search for related documents- accurate diagnosis and lstm network: 1
- accurate diagnosis and lstm network short term memory: 1
- accurate diagnosis and lung background: 1, 2, 3, 4, 5
- accurate diagnosis and lung cancer: 1, 2, 3, 4, 5, 6
- accurate diagnosis and lung cancer diagnosis: 1, 2
- accurate diagnosis and lung detection: 1, 2, 3, 4, 5, 6, 7
- accurate diagnosis and lung disease: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
- accurate diagnosis and lung infection: 1, 2, 3, 4, 5, 6, 7
- accurate diagnosis and lung lesion: 1
- accurate diagnosis and lung model: 1
- accurate diagnosis and lung parenchyma: 1
- accurate diagnosis and lung pathology: 1
- accurate diagnosis and lung region: 1, 2, 3, 4
- accurate diagnosis and lung segmentation: 1, 2
- accurate diagnosis and lung severity: 1, 2
- accurate diagnosis and lung slice: 1, 2
- accurate diagnosis and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43
- accurate diagnosis and machine learning model: 1, 2, 3, 4, 5
- accurate diagnosis and machine learning solution: 1, 2
Co phrase search for related documents, hyperlinks ordered by date