X-Ray Image based COVID-19 Detection using Pre-trained Deep Learning Models

Horry, Michael J.; Chakraborty, Subrata; Paul, Manoranjan; Ulhaq, Anwaar; Pradhan, Biswajeet; Saha, Manash; Shukla, Nagesh

doi:10.31224/osf.io/wx89s

Cited by 71 publications

(59 citation statements)

References 30 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ResNet50 plus SVM is statistically superior to other models with accuracy and the f1-score was 95.38%, 95.52% respectively. Horry et al [29] developed a system based on pre-trained model to detect COVID-19 from chest X-ray. They used Xception, VGG, Resnet, and Inception for the classification of COVID-19 patients.…”

Section: Related Workmentioning

confidence: 99%

A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images

Islam

Asraf

2020

Preprint

View full text Add to dashboard Cite

Nowadays automatic disease detection has become a crucial issue in medical science with the rapid growth of population. Coronavirus (COVID-19) has become one of the most severe and acute diseases in very recent times that has been spread globally. Automatic disease detection framework assists the doctors in the diagnosis of disease and provides exact, consistent, and fast reply as well as reduces the death rate. Therefore, an automated detection system should be implemented as the fastest way of diagnostic option to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short -term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 421 X -ray images including 141 images of COVID-19 is used as a dataset in this system. The experimental results show that our proposed system has achieved 97% accuracy, 91% specificity, and 100% sensitivity. The system achieved desired results on a small dataset which can be further improved when more COVID-19 images become available. The proposed system can assist doctors to diagnose and treatment the COVID-19 patients easily.

show abstract

Section: Related Workmentioning

confidence: 99%

A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images

Islam

Asraf

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The VGG19 obtained high performance among others with an accuracy of 93.48%, specificity of 92.85%, and sensitivity of 98.75%. Horry et al [23] illustrated deep transfer learning based system and achieved the highest result for VGG19 with 83% recall and 83% precision for the diagnosis of COVID-19. Loey et al [24] proposed a deep transfer learning approach with three pre-trained CNN networks to diagnose coronavirus disease.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer Learning

Islam

Asraf

Ding

2020

Preprint

View full text Add to dashboard Cite

The confrontation of COVID-19 pandemic has become one of the promising challenges of the world healthcare. Accurate and fast diagnosis of COVID-19 cases is essential for correct medical treatment to control this pandemic. Compared with the reverse-transcription polymerase chain reaction (RT-PCR) method, chest radiography imaging techniques are shown to be more effective to detect coronavirus. For the limitation of available medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. CNN is used to extract complex features from samples and classified them using RNN. The VGG19-RNN architecture achieved the best performance among all the networks in terms of accuracy and computational time in our experiments. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize class-specific regions of images that are responsible to make decision. The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff.

show abstract

“…Apostolopoulos and Mpesiana (2020) used transfer learning CNN from MobileNetV2 and VGG19 to extract the features of COVID-19, bacterial pneumonia and normal cases and their approach showed an accuracy of 96.78%. Horry et al (2020) used four pre-trained ImageNet models for COVID-19 diagnosis and showed that VGG19 produces good results in prediction with a precision of 83%. Chowdhury et al (2020) used eight pre-trained neural networks to perform diagnosis between normal and COVID-19 and normal, pneumonia from COVID-19 i.e.…”

Section: Introductionmentioning

confidence: 99%

COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking

Elakkiya

Vijayakumar²,

Karuppiah

2021

Inf Syst Front

View full text Add to dashboard Cite

Infectious diseases are highly contagious due to rapid transmission and very challenging to diagnose in the early stage. Artificial Intelligence and Machine Learning now become a strategic weapon in assisting infectious disease prevention, rapid-response in diagnosis, surveillance, and management. In this paper, a bifold COVID_SCREENET architecture is introduced for providing COVID-19 screening solutions using Chest Radiography (CR) images. Transfer learning using nine pre-trained ImageNet models to extract the features of Normal, Pneumonia, and COVID-19 images is adapted in the first fold and classified using baseline Convolutional Neural Network (CNN). A Modified Stacked Ensemble Learning (MSEL) is proposed in the second fold by stacking the top five pre-trained models, and then the predictions resulted. Experimentation is carried out in two folds: In first fold, open-source samples are considered and in second fold 2216 real-time samples collected from Tamilnadu Government Hospitals, India, and the screening results for COVID data is 100% accurate in both the cases. The proposed approach is also validated and blind reviewed with the help of two radiologists at Thanjavur Medical College & Hospitals by collecting 2216 chest X-ray images between the month of April and May. Based on the reports, the measures are calculated for COVID_SCREENET and it showed 100% accuracy in performing multi-class classification.

show abstract

X-Ray Image based COVID-19 Detection using Pre-trained Deep Learning Models

Cited by 71 publications

References 30 publications

A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images

A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images

Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer Learning

COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking

Contact Info

Product

Resources

About