Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia patients: A Deep Learning Perspective

ABSTRACTAs the number of COVID-19 patients has increased worldwide, many efforts have been made to find common patterns in CT images of COVID-19 patients and to confirm the relevance of these patterns against other clinical information. The aim of this paper is to propose a new method that allowed us to find patterns which observed on CTs of patients, and further we use these patterns for disease and severity diagnosis. For the experiment, we performed a retrospective cohort study of 170 confirmed patients with COVID-19 and bacterial pneumonia acquired at Yeungnam University hospital in Daegu, Korea. We extracted lesions inside the lungs from the CT images and classified whether these lesions were from COVID-19 patients or bacterial pneumonia patients by applying a deep learning model. From our experiments, we found 20 patterns that have a major effect on the classification performance of the deep learning model. Crazy-paving was extracted as a major pattern of bacterial pneumonia, while Ground-glass opacities (GGOs) in the peripheral lungs as that of COVID-19. Diffuse GGOs in the central and peripheral lungs was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia with 95% reported for severity classification. Chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. Moreover, the constructed patient level histogram with/without radiomics features showed feasibility and improved accuracy for both disease and severity classification with key clinical implications.

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Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images

Digital Technologies and Applications - Lecture Notes in Networks and Systems ◽

10.1007/978-3-030-73882-2_89 ◽

2021 ◽

pp. 981-990

Author(s):

Mostafa El Habib Daho ◽

Amin Khouani ◽

Mohammed El Amine Lazouni ◽

Sidi Ahmed Mahmoudi

Keyword(s):

Deep Learning ◽

Learning Model ◽

Ct Images ◽

Chest Ct ◽

Deep Learning Model

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Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets

Academic Radiology ◽

10.1016/j.acra.2020.07.030 ◽

2020 ◽

Author(s):

Kazuhiro Suzuki ◽

Yujiro Otsuka ◽

Yukihiro Nomura ◽

Kanako K. Kumamaru ◽

Ryohei Kuwatsuru ◽

...

Keyword(s):

Deep Learning ◽

Three Dimensional ◽

Image Database ◽

Learning Model ◽

Ct Images ◽

Chest Ct ◽

Automated Detection ◽

Lung Nodules ◽

Development And Validation ◽

Deep Learning Model

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COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

10.2196/preprints.19407 ◽

2020 ◽

Author(s):

Jinseok Lee

Keyword(s):

Deep Learning ◽

Diagnostic Performance ◽

Ct Images ◽

Chest Ct ◽

University Hospital ◽

Detection Accuracy ◽

Ct Image ◽

Test Dataset ◽

Learning Framework ◽

Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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MA03.07 Development and Validation of Deep Learning Model for Recognition of Histologic Subtype of Lung Adenocarcinoma from CT Images

Journal of Thoracic Oncology ◽

10.1016/j.jtho.2018.08.336 ◽

2018 ◽

Vol 13 (10) ◽

pp. S364

Author(s):

Y. She ◽

J. Deng ◽

D. Xie ◽

C. Chen

Keyword(s):

Deep Learning ◽

Lung Adenocarcinoma ◽

Learning Model ◽

Ct Images ◽

Histologic Subtype ◽

Development And Validation ◽

Deep Learning Model

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Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

10.1101/2020.04.16.20064709 ◽

2020 ◽

Cited By ~ 5

Author(s):

A. Amyar ◽

R. Modzelewski ◽

S. Ruan

Keyword(s):

Deep Learning ◽

Ct Images ◽

Ct Imaging ◽

Chest Ct ◽

The Novel ◽

Imaging Analysis ◽

Proposed Model ◽

Fast Spreading ◽

Novel Coronavirus ◽

Deep Learning Model

ABSTRACTThe fast spreading of the novel coronavirus COVID-19 has aroused worldwide interest and concern, and caused more than one million and a half confirmed cases to date. To combat this spread, medical imaging such as computed tomography (CT) images can be used for diagnostic. An automatic detection tools is necessary for helping screening COVID-19 pneumonia using chest CT imaging. In this work, we propose a multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Our motivation is to leverage useful information contained in multiple related tasks to help improve both segmentation and classification performances. Our architecture is composed by an encoder and two decoders for reconstruction and segmentation, and a multi-layer perceptron for classification. The proposed model is evaluated and compared with other image segmentation and classification techniques using a dataset of 1044 patients including 449 patients with COVID-19, 100 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.78 for the segmentation and an area under the ROC curve higher than 93% for the classification.

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