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

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%.

scholarly journals Detecting Coronavirus from Chest X-rays Using Transfer Learning

COVID ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 403-415
Author(s):  
Abeer Badawi ◽  
Khalid Elgazzar
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Healthcare Providers ◽  
Large Set ◽  
X Rays ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Multi Class Classification ◽  
Novel Coronavirus

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus family. One of the practical examinations for COVID-19 is chest radiography. COVID-19 infected patients show abnormalities in chest X-ray images. However, examining the chest X-rays requires a specialist with high experience. Hence, using deep learning techniques in detecting abnormalities in the X-ray images is presented commonly as a potential solution to help diagnose the disease. Numerous research has been reported on COVID-19 chest X-ray classification, but most of the previous studies have been conducted on a small set of COVID-19 X-ray images, which created an imbalanced dataset and affected the performance of the deep learning models. In this paper, we propose several image processing techniques to augment COVID-19 X-ray images to generate a large and diverse dataset to boost the performance of deep learning algorithms in detecting the virus from chest X-rays. We also propose innovative and robust deep learning models, based on DenseNet201, VGG16, and VGG19, to detect COVID-19 from a large set of chest X-ray images. A performance evaluation shows that the proposed models outperform all existing techniques to date. Our models achieved 99.62% on the binary classification and 95.48% on the multi-class classification. Based on these findings, we provide a pathway for researchers to develop enhanced models with a balanced dataset that includes the highest available COVID-19 chest X-ray images. This work is of high interest to healthcare providers, as it helps to better diagnose COVID-19 from chest X-rays in less time with higher accuracy.

scholarly journals Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning

2020 ◽  
Author(s):  
Arshia Rehman ◽  
Saeeda Naz ◽  
Ahmed Khan ◽  
Ahmad Zaib ◽  
Imran Razzak
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Bacterial Pneumonia ◽  
State Of The Art ◽  
High Accuracy ◽  
X Rays ◽  
Early Screening ◽  
X Ray ◽  
Learning Techniques ◽  
The World

AbstractBackgroundCoronavirus disease (COVID-19) is an infectious disease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world.AimThe aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia and healthy cases using deep learning techniques.MethodIn this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance different CNN architectures.ResultsEvaluation results using K-fold (10) showed that we have achieved state of the art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole.ConclusionQuantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models develop in this study could be used early screening of coronavirus, however it calls for extensive need to CT or X-rays dataset to develop a reliable application.

scholarly journals Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6655
Author(s):  
Michael Horry ◽  
Subrata Chakraborty ◽  
Biswajeet Pradhan ◽  
Manoranjan Paul ◽  
Douglas Gomes ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Computer Vision ◽  
Deep Learning ◽  
Decision Tree ◽  
Decision Trees ◽  
Lung Nodules ◽  
X Rays ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

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 COVID-19 Data Analysis using Chest X-ray

2021 ◽  
Vol 1 (4) ◽  
pp. 5-10
Author(s):  
Ishtiaque Ahmed ◽  
◽  
Manan Darda ◽  
Neha Tikyani ◽  
Rachit Agrawal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Predictive Value ◽  
Large Scale ◽  
X Rays ◽  
Learning Models ◽  
Data Set ◽  
X Ray ◽  
Dataset Size ◽  
Chest X Ray

The COVID-19 pandemic has caused large-scale outbreaks in more than 150 countries worldwide, causing massive damage to the livelihood of many people. The capacity to identify contaminated patients early and get unique treatment is quite possibly the primary stride in the battle against COVID-19. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect the disease. Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities. To identify COVID-19 patients from chest X-ray images, we used various deep learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources. The model produces reliable and stable results with an accuracy of 91.6%, a Positive Predictive Value of 80%, a Negative Predictive Value of 100%, specificity of 87.50%, and Sensitivity of 100%. It is observed that the CNN-based architecture can diagnose COVID19 disease. The parameters’ outcomes can be further improved by increasing the dataset size and by developing the CNN-based architecture for training the model.

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

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.

scholarly journals COVID-19 Data Analysis using Chest X-Ray

2021 ◽  
pp. 5-10
Author(s):  
Ishtiaque Ahmed ◽  
◽  
Manan Darda ◽  
Neha Tikyani ◽  
Rachit Agrawal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Predictive Value ◽  
Large Scale ◽  
X Rays ◽  
Learning Models ◽  
Data Set ◽  
X Ray ◽  
Dataset Size ◽  
Chest X Ray

The COVID-19 pandemic has caused large-scale outbreaks in more than 150 countries worldwide, causing massive damage to the livelihood of many people. The capacity to identify contaminated patients early and get unique treatment is quite possibly the primary stride in the battle against COVID-19. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect the disease. Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities. To identify COVID-19 patients from chest X-ray images, we used various deep learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources. The model produces reliable and stable results with an accuracy of 91.6%, a Positive Predictive Value of 80%, a Negative Predictive Value of 100%, specificity of 87.50%, and Sensitivity of 100%. It is observed that the CNN-based architecture can diagnose COVID-19 disease. The parameters’ outcomes can be further improved by increasing the dataset size and by developing the CNN-based architecture for training the model.

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

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.

COVID-19 Detection Using Chest X-Ray and Transfer Learning

2021 ◽  
pp. 171-186
Author(s):  
Aditya Sharma ◽  
Arshdeep Singh Chudey ◽  
Mrityunjay Singh
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Confusion Matrix ◽  
Receiver Operating Curve ◽  
The Novel ◽  
Learning Models ◽  
X Ray ◽  
Major Outbreak ◽  
Chest X Ray ◽  
Novel Coronavirus

The novel coronavirus (COVID-19), which started in the Wuhan province of China, prompted a major outbreak that culminated in a worldwide pandemic. Several cases are being recorded across the globe, with deaths being close to 2.5 million. The increased number of cases and the newness of such a pandemic has resulted in the hospitals being under-equipped leading to problems in diagnosis of the disease. From previous studies, radiography has proved to be the fastest testing method. A screening test using the x-ray scan of the chest region has proved to be effective. For this method, a trained radiologist is needed to detect the disease. Automating this process using deep learning models can prove to be effective. Due to the lack of large dataset, pre-trained CNN models are used in this study. Several models have been employed like VGG-16, Resnet-50, InceptionV3, and InceptionResnetV2. Resnet-50 provided the best accuracy of 98.3%. The performance evaluation has been done using metrics like receiver operating curve and confusion matrix.

scholarly journals COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1972
Author(s):  
Abul Bashar ◽  
Ghazanfar Latif ◽  
Ghassen Ben Brahim ◽  
Nazeeruddin Mohammad ◽  
Jaafar Alghazo
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Learning Algorithm ◽  
The Body ◽  
X Rays ◽  
X Ray ◽  
Clinical Observations ◽  
Dataset Size ◽  
Chest X Ray

It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.

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