Deep Learning to Improve COVID-19 Detection: Using CNN-Based Transfer Learning from Chest X-Ray Images

According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.

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

PeerJ ◽

10.7717/peerj.10309 ◽

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.

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Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Transfer Learning

10.1101/2020.05.27.20100297 ◽

2020 ◽

Author(s):

Sarath Pathari ◽

Rahul U

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Clinical Picture ◽

Bacterial Pneumonia ◽

Automatic Detection ◽

Neural System ◽

Cutting Edge ◽

X Ray ◽

X Ray Imaging ◽

Chest X Ray

In this study, a dataset of X-ray images from patients with common viral pneumonia, bacterial pneumonia, confirmed Covid-19 disease was utilized for the automatic detection of the Coronavirus disease. The point of the investigation is to assess the exhibition of cutting edge convolutional neural system structures proposed over the ongoing years for clinical picture order. In particular, the system called Transfer Learning was received. With transfer learning, the location of different variations from the norm in little clinical picture datasets is a reachable objective, regularly yielding amazing outcomes. The datasets used in this trial. Firstly, a collection of 24000 X-ray images includes 6000 images for confirmed Covid-19 disease,6000 confirmed common bacterial pneumonia and 6000 images of normal conditions. The information was gathered and expanded from the accessible X-Ray pictures on open clinical stores. The outcomes recommend that Deep Learning with X-Ray imaging may separate noteworthy biomarkers identified with the Covid-19 sickness, while the best precision, affectability, and particularity acquired is 97.83%, 96.81%, and 98.56% individually.

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

10.1101/2020.09.02.20186759 ◽

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.

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A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning

Journal of Healthcare Engineering ◽

10.1155/2021/1002799 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Omar Faruk ◽

Eshan Ahmed ◽

Sakil Ahmed ◽

Anika Tabassum ◽

Tahia Tazin ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Data Augmentation ◽

Computer Assisted ◽

Promising Technique ◽

Suggested Approach ◽

X Ray ◽

Chest X Ray ◽

Deep Learning Model

Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated, and evaluated for the classification of tuberculosis and nontuberculosis cases using transfer learning from their pretrained starting weights. With an F1-score of 99 percent, InceptionResNetV2 had the highest accuracy. This research is more accurate than earlier published work. Additionally, it outperforms all other models in terms of reliability. The suggested approach, with its state-of-the-art performance, may be helpful for computer-assisted rapid TB detection.

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Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/8036304 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Surya Krishnamurthy ◽

Kathiravan Srinivasan ◽

Saeed Mian Qaisar ◽

P. M. Durai Raj Vincent ◽

Chuan-Yu Chang

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Large Scale ◽

Data Augmentation ◽

Medical Image Analysis ◽

Predictive Performance ◽

X Ray ◽

Recent Developments ◽

Life Threatening ◽

Chest X Ray

Pneumonitis is an infectious disease that causes the inflammation of the air sac. It can be life-threatening to the very young and elderly. Detection of pneumonitis from X-ray images is a significant challenge. Early detection and assistance with diagnosis can be crucial. Recent developments in the field of deep learning have significantly improved their performance in medical image analysis. The superior predictive performance of the deep learning methods makes them ideal for pneumonitis classification from chest X-ray images. However, training deep learning models can be cumbersome and resource-intensive. Reusing knowledge representations of public models trained on large-scale datasets through transfer learning can help alleviate these challenges. In this paper, we compare various image classification models based on transfer learning with well-known deep learning architectures. The Kaggle chest X-ray dataset was used to evaluate and compare our models. We apply basic data augmentation and fine-tune our feed-forward classification head on the models pretrained on the ImageNet dataset. We observed that the DenseNet201 model outperforms other models with an AUROC score of 0.966 and a recall score of 0.99. We also visualize the class activation maps from the DenseNet201 model to interpret the patterns recognized by the model for prediction.

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Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms

Journal of X-Ray Science and Technology ◽

10.3233/xst-200720 ◽

2020 ◽

Vol 28 (5) ◽

pp. 841-850

Author(s):

Saleh Albahli ◽

Waleed Albattah

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Data Augmentation ◽

Medical Image Analysis ◽

Image Data ◽

Network Models ◽

Learning Models ◽

X Ray ◽

Study Results ◽

Chest X Ray

OBJECTIVE: This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of novel coronavirus (COVID-19) infected disease. METHOD: This study applied transfer learning method to develop deep learning models for detecting COVID-19 disease. Three existing state-of-the-art deep learning models namely, Inception ResNetV2, InceptionNetV3 and NASNetLarge, were selected and fine-tuned to automatically detect and diagnose COVID-19 disease using chest X-ray images. A dataset involving 850 images with the confirmed COVID-19 disease, 500 images of community-acquired (non-COVID-19) pneumonia cases and 915 normal chest X-ray images was used in this study. RESULTS: Among the three models, InceptionNetV3 yielded the best performance with accuracy levels of 98.63% and 99.02% with and without using data augmentation in model training, respectively. All the performed networks tend to overfitting (with high training accuracy) when data augmentation is not used, this is due to the limited amount of image data used for training and validation. CONCLUSION: This study demonstrated that a deep transfer learning is feasible to detect COVID-19 disease automatically from chest X-ray by training the learning model with chest X-ray images mixed with COVID-19 patients, other pneumonia affected patients and people with healthy lungs, which may help doctors more effectively make their clinical decisions. The study also gives an insight to how transfer learning was used to automatically detect the COVID-19 disease. In future studies, as the amount of available dataset increases, different convolution neutral network models could be designed to achieve the goal more efficiently.

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Comparison of Classification Models Based on Deep learning on COVID-19 Chest X-Rays

Journal of Physics Conference Series ◽

10.1088/1742-6596/2161/1/012078 ◽

2022 ◽

Vol 2161 (1) ◽

pp. 012078

Author(s):

Pallavi R Mane ◽

Rajat Shenoy ◽

Ghanashyama Prabhu

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

State Of The Art ◽

Cost Effective ◽

X Rays ◽

The Novel ◽

Learning Models ◽

X Ray ◽

Chest X Ray ◽

Fine Tune

Abstract COVID -19, is a deadly, dangerous and contagious disease caused by the novel corona virus. It is very important to detect COVID-19 infection accurately as quickly as possible to avoid the spreading. Deep learning methods can significantly improve the efficiency and accuracy of reading Chest X-Rays (CXRs). The existing Deep learning models with further fine tune provide cost effective, rapid, and better classification results. This paper tries to deploy well studied AI tools with modification on X-ray images to classify COVID 19. This research performs five experiments to classify COVID-19 CXRs from Normal and Viral Pneumonia CXRs using Convolutional Neural Networks (CNN). Four experiments were performed on state-of-the-art pre-trained models using transfer learning and one experiment was performed using a CNN designed from scratch. Dataset used for the experiments consists of chest X-Ray images from the Kaggle dataset and other publicly accessible sources. The data was split into three parts while 90% retained for training the models, 5% each was used in validation and testing of the constructed models. The four transfer learning models used were Inception, Xception, ResNet, and VGG19, that resulted in the test accuracies of 93.07%, 94.8%, 67.5%, and 91.1% respectively and our CNN model resulted in 94.6%.

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Transfer Learning Based Multiclass Classification For Covid-19 Detection Using Chest X-Rays

10.21203/rs.3.rs-810768/v1 ◽

2021 ◽

Author(s):

Japman Singh Monga ◽

Yuvraj Singh Champawat ◽

Seema Kharb

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

X Rays ◽

Performance Parameters ◽

Chain Reaction ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Polymerase Chain ◽

Deep Learning Model

Abstract In the year 2020 world came to a halt due to spread of Covid-19 or SARS-CoV2 which was first identified in Wuhan, China. Since then, it has caused plethora of problems around the globe such as loss of millions of lives, economic instability etc. Less effectiveness of detection through Reverse Transcription Polymerase Chain Reaction and also prolonged time needed for detection through the same calls for a substitute for Covid-19 detection. Hence, in this study, we aim to develop a transfer learning based multi-class classifier using Chest X-Ray images which will classify the X-Ray images in 3 classes (Covid-19, Pneumonia, Normal). Further, the proposed model has been trained with deep learning classifiers namely: DenseNet201, Xception, ResNet50V2, VGG16, VGG-19, InceptionResNetV2 .These are evaluated on the basis of accuracy, precision and recall as performance parameters. It has been observed that DenseNet201 is the best deep learning model with 82.2% accuracy.

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