scholarly journals An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Aijaz Ahmad Reshi ◽  
Furqan Rustam ◽  
Arif Mehmood ◽  
Abdulaziz Alhossan ◽  
Ziyad Alrabiah ◽  
...  
Keyword(s):  
Image Analysis ◽  
Image Classification ◽  
Data Augmentation ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Test Scenario ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X-ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set.

Chest X-ray Image Analysis and Penehyclidine Hydrochloride Postconditioning Influence of Lung Ischemia Reperfusion Injury

2020 ◽  
Vol 10 (12) ◽  
pp. 2834-2841
Author(s):  
Chunlei Zhang ◽  
Jun Ma
Keyword(s):  
Image Analysis ◽  
Cardiopulmonary Bypass ◽  
Lung Function ◽  
Heart Surgery ◽  
Mapk Pathway ◽  
Medical Image Analysis ◽  
High Dose ◽  
X Ray ◽  
Chest X Ray ◽  
Penehyclidine Hydrochloride

Purpose: We analyze chest X-ray diagnosis results of patients undergoing cardiopulmonary bypass heart surgery in intensive care unit (ICU). In our previous study, we found that penehyclidine hydrochloride (PHC) preconditioning pretects lung function and reduced the apoptosis. Although preconditioning is effective, this clinical treatment is often given only when symptoms appear, when postconditioning is easier to administer. In this study, after confirming the lung condition via medical image analysis, we aimed to look the effect of high-dose penehyclidine hydrochloride postconditioning in lung after I/R in rats, and the apoptosis mechanisms involved. Methods: Chest X-ray was taken in 256 adult patients under cardiopulmonary bypass with heart syurery. Medical image analysis was preliminarily performed, the diagnostic results were analyzed. Rats were subjected to ischemia in left lung 45 min, and then 2 h reperfusion, and treated with PHC. We then observed the effects of PHC on the following: lung function, lung injury, oxidative stress, rate of apoptosis of lung cells, apoptosis-related proteins, and the p38 MAPK pathway. Results: Analysis results of chest X-ray suggest multiple pulmonary complications after cardiopulmonary bypass heart surgery. The laboratory results showed that high-dose penehyclidine hydrochloride postconditioning significantly protected lung function, reduced oxidative stress and apoptosis of the lungs caused by reperfusion, and inhibited activation of the p38 MAPK pathway. Conclusion: Analysis results of chest X-ray show multiple pulmonary complications after cardiopulmonary bypass heart surgery. This study suggests that high-dose penehyclidine hydrochloride can treat lung I/R injury.

scholarly journals Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems

2020 ◽  
Vol 16 ◽  
Author(s):  
Saleh Albahli ◽  
Waleed Albattah
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
High Performance ◽  
Data Augmentation ◽  
Medical Image Analysis ◽  
Medical Community ◽  
X Ray ◽  
Normal Chest ◽  
Chest X Ray ◽  
Performance Results

Objective: Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images. Method: This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community. Result: Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation. Conclusion: A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest Xrays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

scholarly journals Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis

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.

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 Deep Learning Model for Improving the Characterization of Coronavirus on Chest X-ray Images Using CNN

2020 ◽  
Author(s):  
Olaide N. Oyelade ◽  
Absalom E. Ezugwu
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Data Augmentation ◽  
Knowledge Engineering ◽  
The Novel ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray ◽  
Novel Coronavirus

AbstractThe novel Coronavirus, also known as Covid19, is a pandemic that has weighed heavily on the socio-economic affairs of the world. Although researches into the production of relevant vaccine are being advanced, there is, however, a need for a computational solution to mediate the process of aiding quick detection of the disease. Different computational solutions comprised of natural language processing, knowledge engineering and deep learning have been adopted for this task. However, deep learning solutions have shown interesting performance compared to other methods. This paper therefore aims to advance the application deep learning technique to the problem of characterization and detection of novel coronavirus. The approach adopted in this study proposes a convolutional neural network (CNN) model which is further enhanced using the technique of data augmentation. The motive for the enhancement of the CNN model through the latter technique is to investigate the possibility of further improving the performances of deep learning models in detection of coronavirus. The proposed model is then applied to the COVID-19 X-ray dataset in this study which is the National Institutes of Health (NIH) Chest X-Ray dataset obtained from Kaggle for the purpose of promoting early detection and screening of coronavirus disease. Results obtained showed that our approach achieved a performance of 100% accuracy, recall/precision of 0.85, F-measure of 0.9, and specificity of 1.0. The proposed CNN model and data augmentation solution may be adopted in pre-screening suspected cases of Covid19 to provide support to the use of the well-known RT-PCR testing.

Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization

2022 ◽  
Vol 12 (2) ◽  
pp. 1904
Author(s):  
Farah Flayeh Alkhalid ◽  
Abdulhakeem Qusay Albayati ◽  
Ahmed Ali Alhammad
Keyword(s):  
Data Augmentation ◽  
State Of The Art ◽  
Virus Disease ◽  
Histogram Equalization ◽  
Vital Role ◽  
X Ray ◽  
Current State ◽  
Proposed Model ◽  
Chest X Ray ◽  
Using Data

The main important factor that plays vital role in success the deep learning is the deep training by many and many images, if neural networks are getting bigger and bigger but the training datasets are not, then it sounds like going to hit an accuracy wall. Briefly, this paper investigates the current state of the art of approaches used for a data augmentation for expansion the corona virus disease 2019 (COVID-19) chest X-ray images using different data augmentation methods (transformation and enhancement) the dataset expansion helps to rise numbers of images from 138 to 5520, the increasing rate is 3,900%, this proposed model can be used to expand any type of image dataset, in addition, the dataset have used with convolutional neural network (CNN) model to make classification if detected infection with COVID-19 in X-ray, the results have gotten high training accuracy=99%

Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Tengku Afiah Mardhiah Tengku Zainul Akmal ◽  
Joel Chia Ming Than ◽  
Haslailee Abdullah ◽  
Norliza Mohd Noor
Keyword(s):  
Image Classification ◽  
X Ray ◽  
Chest X Ray

scholarly journals XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Vishu Madaan ◽  
Aditya Roy ◽  
Charu Gupta ◽  
Prateek Agrawal ◽  
Anand Sharma ◽  
...  
Keyword(s):  
Image Classification ◽  
Second Phase ◽  
Two Phase ◽  
X Ray ◽  
The Neural Network ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Two Phases ◽  
Active Patients

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

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