scholarly journals Deep learning to distinguish COVID-19 from other lung infections, pleural diseases, and lung tumors

2020 ◽  
Author(s):  
Ali Serener ◽  
Sertan Serte
Keyword(s):  
Deep Learning ◽  
Lung Tumors ◽  
Pleural Diseases ◽  
Lung Infections

scholarly journals Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10309
Author(s):  
Shreeja Kikkisetti ◽  
Jocelyn Zhu ◽  
Beiyi Shen ◽  
Haifang Li ◽  
Tim Q. Duong
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Bacterial Pneumonia ◽  
Lung Infection ◽  
Accurate Diagnosis ◽  
Viral Pneumonia ◽  
X Ray ◽  
Portable Chest ◽  
Chest X Ray ◽  
Lung Infections

Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N = 455) on pCXR from normal (N = 532), bacterial pneumonia (N = 492), and non-COVID viral pneumonia (N = 552). The data was randomly split into 75% training and 25% testing, randomly. A five-fold cross-validation was used for the testing set separately. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis.

scholarly journals COVID-19 detection in Chest X-ray Images using Deep Learning

2020 ◽  
Author(s):  
Ebru Erdem ◽  
Tolga Aydın
Keyword(s):  
Deep Learning ◽  
Deep Neural Networks ◽  
Empirical Evaluation ◽  
Specific Information ◽  
The Novel ◽  
X Ray ◽  
Chest X Ray ◽  
Lung Infections ◽  
Day By Day

Abstract COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All the analyses were performed on a publicly available CXR dataset. This study offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of a disease. Empirical evaluation demonstrates that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models. In addition to the studies in the literature, it has been observed that the proposed pre-trained-based concatenated model gives very similar successful results to the other models.

scholarly journals Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning

Tomography ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 358-372
Author(s):  
Matthew D. Holbrook ◽  
Darin P. Clark ◽  
Rutulkumar Patel ◽  
Yi Qi ◽  
Alex M. Bassil ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Nodule ◽  
Model Performance ◽  
Area Under The Curve ◽  
Simulated Data ◽  
Real Data ◽  
Lung Tumors ◽  
Training Data ◽  
Micro Ct ◽  
Micro Computed Tomography

We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76–0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice.

scholarly journals COVID-19 detection in Chest X-ray Images using Deep Learning

2020 ◽  
Author(s):  
Ebru Erdem ◽  
Tolga Aydın
Keyword(s):  
Deep Learning ◽  
Deep Neural Networks ◽  
Specific Information ◽  
The Novel ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Lung Infections ◽  
Day By Day

Abstract COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All analyzes were performed on CXR data received on the publicly available. This paper offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of disease. Empirical evaluations demonstrate that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models.

scholarly journals Deep-learning convolutional neural networks with transfer learning accurately classify COVID19 lung infection on portable chest radiographs

2020 ◽  
Author(s):  
Shreeja Kikkisetti ◽  
Jocelyn Zhu ◽  
Beiyi Shen ◽  
Haifang Li ◽  
Tim Duong
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Bacterial Pneumonia ◽  
Lung Infection ◽  
Accurate Diagnosis ◽  
Viral Pneumonia ◽  
X Ray ◽  
Portable Chest ◽  
Chest X Ray ◽  
Lung Infections

Portable chest x-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N=455) on pCXR from normal (N=532), bacterial pneumonia (N=492), and non-COVID viral pneumonia (N=552). The data was split into 75% training and 25% testing. A five-fold cross-validation was used. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest x-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis.

scholarly journals Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning

2016 ◽  
Vol 4 ◽  
pp. 1-12 ◽  
Author(s):  
Seonyeong Park ◽  
Suk Jin Lee ◽  
Elisabeth Weiss ◽  
Yuichi Motai
Keyword(s):  
Deep Learning ◽  
Lung Tumors

scholarly journals Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm

2019 ◽  
Vol 46 (10) ◽  
pp. 4699-4707 ◽  
Author(s):  
Chuang Wang ◽  
Andreas Rimner ◽  
Yu‐Chi Hu ◽  
Neelam Tyagi ◽  
Jue Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mr Imaging ◽  
Learning Algorithm ◽  
Imaging Study ◽  
Lung Tumors ◽  
Deep Learning Algorithm

scholarly journals A frame work for the detection and Diagnosis of Lung Tumors using Deep learning Methods

2021 ◽  
Vol 12 (2) ◽  
pp. 2404-2409
Author(s):  
P.Jagadeesh , Et. al.
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Lung Tumors ◽  
Ct Images ◽  
Segmentation Method ◽  
Low Contrast ◽  
Frame Work ◽  
Detection And Diagnosis ◽  
Learning Architectures ◽  
Lung Ct

The detection of tumor pixels in lung images is complex task due to its low contrast property. Hence, this paper uses deep learning architectures for both the detection and diagnosis of lung tumors in Computer Tomography (CT) images. In this article, the tumors are detected in lung CT images using Convolutional Neural Networks (CNN) architecture with the help of data augmentation methods. This proposed CNN architecture classifies the lung images into two categories as tumor images and normal images. Then, the segmentation method is used to segment the tumor pixels in the lung CT images and the segmented tumor regions are classified into either mild or severe using proposed CNN architecture.

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