scholarly journals COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Hoon Ko ◽  
Heewon Chung ◽  
Wu Seong Kang ◽  
Kyung Won Kim ◽  
Youngbin Shin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Data Set ◽  
Learning Framework ◽  
Testing Data

BACKGROUND Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied 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 rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non–COVID-19 pneumonia and nonpneumonia diseases. METHODS A simple 2D deep learning framework, named the 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 four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia 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 set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS 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 data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

scholarly journals COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation

10.2196/19569 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e19569 ◽  
Author(s):  
Hoon Ko ◽  
Heewon Chung ◽  
Wu Seong Kang ◽  
Kyung Won Kim ◽  
Youngbin Shin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Data Set ◽  
Learning Framework ◽  
Testing Data

Background Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied 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 rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non–COVID-19 pneumonia and nonpneumonia diseases. Methods A simple 2D deep learning framework, named the 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 four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia 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 set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. Results Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). Conclusions 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 data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

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

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.

scholarly journals ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 268
Author(s):  
Yeganeh Jalali ◽  
Mansoor Fateh ◽  
Mohsen Rezvani ◽  
Vahid Abolghasemi ◽  
Mohammad Hossein Anisi
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Great Success ◽  
Morphological Operations ◽  
Ct Image ◽  
Feature Maps ◽  
Learning Framework ◽  
Lung Ct

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.

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

2020 ◽  
Author(s):  
Myeongkyun Kang ◽  
Philip Chikontwe ◽  
Miguel Luna ◽  
Kyung Soo Hong ◽  
Jong Geol Jang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bacterial Pneumonia ◽  
Clinical Information ◽  
Learning Model ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Severity Classification ◽  
Ct Analysis ◽  
Deep Learning Model

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.

scholarly journals Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images

2019 ◽  
Vol 48 (6) ◽  
pp. 20190019 ◽  
Author(s):  
Yoshitaka Kise ◽  
Haruka Ikeda ◽  
Takeshi Fujii ◽  
Motoki Fukuda ◽  
Yoshiko Ariji ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Sjögren's Syndrome ◽  
Sjogren’S Syndrome ◽  
Ct Images ◽  
Sjogren's Syndrome ◽  
Learning System ◽  
Data Set ◽  
Control Subjects ◽  
Sjögren‘S Syndrome

Objectives: This study estimated the diagnostic performance of a deep learning system for detection of Sjögren's syndrome (SjS) on CT, and compared it with the performance of radiologists. Methods: CT images were assessed from 25 patients confirmed to have SjS based on the both Japanese criteria and American-European Consensus Group criteria and 25 control subjects with no parotid gland abnormalities who were examined for other diseases. 10 CT slices were obtained for each patient. From among the total of 500 CT images, 400 images (200 from 20 SjS patients and 200 from 20 control subjects) were employed as the training data set and 100 images (50 from 5 SjS patients and 50 from 5 control subjects) were used as the test data set. The performance of a deep learning system for diagnosing SjS from the CT images was compared with the diagnoses made by six radiologists (three experienced and three inexperienced radiologists). Results: The accuracy, sensitivity, and specificity of the deep learning system were 96.0%, 100% and 92.0%, respectively. The corresponding values of experienced radiologists were 98.3%, 99.3% and 97.3% being equivalent to the deep learning, while those of inexperienced radiologists were 83.5%, 77.9% and 89.2%. The area under the curve of inexperienced radiologists were significantly different from those of the deep learning system and the experienced radiologists. Conclusions: The deep learning system showed a high diagnostic performance for SjS, suggesting that it could possibly be used for diagnostic support when interpreting CT images.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals A Novel Deep Learning–Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study

10.2196/27008 ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. e27008
Author(s):  
Li-Hung Yao ◽  
Ka-Chun Leung ◽  
Chu-Lin Tsai ◽  
Chien-Hua Huang ◽  
Li-Chen Fu
Keyword(s):  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Severity Index ◽  
University Hospital ◽  
Emergency Severity Index ◽  
National Hospital ◽  
Data Set ◽  
Care Survey ◽  
Ambulatory Medical Care

Background Emergency department (ED) crowding has resulted in delayed patient treatment and has become a universal health care problem. Although a triage system, such as the 5-level emergency severity index, somewhat improves the process of ED treatment, it still heavily relies on the nurse’s subjective judgment and triages too many patients to emergency severity index level 3 in current practice. Hence, a system that can help clinicians accurately triage a patient’s condition is imperative. Objective This study aims to develop a deep learning–based triage system using patients’ ED electronic medical records to predict clinical outcomes after ED treatments. Methods We conducted a retrospective study using data from an open data set from the National Hospital Ambulatory Medical Care Survey from 2012 to 2016 and data from a local data set from the National Taiwan University Hospital from 2009 to 2015. In this study, we transformed structured data into text form and used convolutional neural networks combined with recurrent neural networks and attention mechanisms to accomplish the classification task. We evaluated our performance using area under the receiver operating characteristic curve (AUROC). Results A total of 118,602 patients from the National Hospital Ambulatory Medical Care Survey were included in this study for predicting hospitalization, and the accuracy and AUROC were 0.83 and 0.87, respectively. On the other hand, an external experiment was to use our own data set from the National Taiwan University Hospital that included 745,441 patients, where the accuracy and AUROC were similar, that is, 0.83 and 0.88, respectively. Moreover, to effectively evaluate the prediction quality of our proposed system, we also applied the model to other clinical outcomes, including mortality and admission to the intensive care unit, and the results showed that our proposed method was approximately 3% to 5% higher in accuracy than other conventional methods. Conclusions Our proposed method achieved better performance than the traditional method, and its implementation is relatively easy, it includes commonly used variables, and it is better suited for real-world clinical settings. It is our future work to validate our novel deep learning–based triage algorithm with prospective clinical trials, and we hope to use it to guide resource allocation in a busy ED once the validation succeeds.

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