scholarly journals Hybrid-COVID: A novel hybrid 2D/3D CNN based on cross-domain adaptation approach for COVID-19 screening from chest X-ray images

2020 ◽  
Author(s):  
Khaled Bayoudh ◽  
Fayçal Hamdaoui ◽  
Abdellatif Mtibaa
Keyword(s):  
Domain Adaptation ◽  
Respiratory Infections ◽  
Screening Methods ◽  
X Ray ◽  
Deep Model ◽  
Learning Techniques ◽  
Spatial Pyramid Pooling ◽  
Chest X Ray ◽  
3D Cnn ◽  
Novel Coronavirus

Abstract So far, COVID-19, the novel coronavirus, continues to spread rapidly in most countries of the world, putting people's lives at risk. According to the WHO, respiratory infections occur primarily in the majority of patients treated with COVID-19. For decades, chest X-ray (CXR) technologies have proven their ability to accurately detect and treat respiratory diseases. Deep learning techniques, as well as the availability of a large number of CXR samples, have made a significant contribution to the fight against this pandemic. However, the most common screening methods are based on 2D CNNs, since 3D counterparts are enormously costly and labor-intensive. In this study, a hybrid 2D/3D convolutional neural network (CNN) architecture for COVID-19 screening using CXRs has been developed. The proposed architecture consists of the incorporation of a pre-trained deep model (VGG-16) and a shallow 3D CNN, combined with a depth-wise separable convolution layer and a spatial pyramid pooling module (SPP). Specifically, the depth-wise separable convolution helps to preserve the useful features while reducing the computational burden of the model. The SPP module is designed to extract multi-level representations from intermediate ones. Experimental results show that the proposed framework can achieve reasonable performances when evaluated on a collected dataset (3 classes: COVID-19, Pneumonia, and Normal). Notably, it achieved a sensitivity of 98.33%, a specificity of 98.68% and an overall accuracy of 96.91%

scholarly journals A holistic comparison between deep learning techniques to determine Covid-19 patients utilizing chest X-Ray images

2020 ◽  
Vol 3 (4) ◽  
pp. 85-93
Author(s):  
Taki Hasan Rafi ◽  
Keyword(s):  
Deep Learning ◽  
Medical Community ◽  
Test Cases ◽  
Suitable Model ◽  
Testing Time ◽  
Rt Pcr ◽  
X Ray ◽  
Learning Techniques ◽  
Chest X Ray ◽  
Novel Coronavirus

Novel coronavirus likewise called COVID-19 began in Wuhan, China in December 2019 and has now outspread over the world. Around 63 millions of people currently got influenced by novel coronavirus and it causes around 1,500,000 deaths. There are just about 600,000 individuals contaminated by COVID-19 in Bangladesh too. As it is an exceptionally new pandemic infection, its diagnosis is challenging for the medical community. In regular cases, it is hard for lower incoming countries to test cases easily. RT-PCR test is the most generally utilized analysis framework for COVID-19 patient detection. However, by utilizing X-ray image based programmed recognition can diminish the expense and testing time. So according to handling this test, it is important to program and effective recognition to forestall transmission to others. In this paper, author attempts to distinguish COVID-19 patients by chest X-ray images. Author executes various pre-trained deep learning models on the dataset such as Base-CNN, ResNet-50, DenseNet-121 and EfficientNet-B4. All the outcomes are compared to determine a suitable model for COVID-19 detection using chest X-ray images. Author also evaluates the results by AUC, where EfficientNet-B4 has 0.997 AUC, ResNet-50 has 0.967 AUC, DenseNet-121 has 0.874 AUC and the Base-CNN model has 0.762 AUC individually. The EfficientNet-B4 has achieved 98.86% accuracy.

scholarly journals COVID-19 infection map generation and detection from chest X-ray images

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Aysen Degerli ◽  
Mete Ahishali ◽  
Mehmet Yamac ◽  
Serkan Kiranyaz ◽  
Muhammad E. H. Chowdhury ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Clinical Use ◽  
X Ray ◽  
Learning Techniques ◽  
Map Generation ◽  
Severity Grading ◽  
Chest X Ray ◽  
Novel Method ◽  
Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

Chest X-Ray Image-Based Testing Using Machine Learning Techniques

Covid-19 ◽  
2021 ◽  
pp. 241-278
Author(s):  
Parag Verma ◽  
Ankur Dumka ◽  
Alaknanda Ashok ◽  
Amit Dumka ◽  
Anuj Bhardwaj
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
X Ray ◽  
Learning Techniques ◽  
Chest X Ray

scholarly journals COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

2020 ◽  
Vol 2 (4) ◽  
pp. 490-504
Author(s):  
Md Manjurul Ahsan ◽  
Kishor Datta Gupta ◽  
Mohammad Maminur Islam ◽  
Sajib Sen ◽  
Md. Lutfar Rahman ◽  
...  
Keyword(s):  
Ct Scan ◽  
Initial Screening ◽  
X Ray ◽  
Learning Techniques ◽  
Alternative Approach ◽  
Number Of Patients ◽  
The Usa ◽  
Chest X Ray ◽  
Interpretable Model ◽  
Potential Opportunity

The outbreak of COVID-19 has caused more than 200,000 deaths so far in the USA alone, which instigates the necessity of initial screening to control the spread of the onset of COVID-19. However, screening for the disease becomes laborious with the available testing kits as the number of patients increases rapidly. Therefore, to reduce the dependency on the limited test kits, many studies suggested a computed tomography (CT) scan or chest radiograph (X-ray) based screening system as an alternative approach. Thereby, to reinforce these approaches, models using both CT scan and chest X-ray images need to develop to conduct a large number of tests simultaneously to detect patients with COVID-19 symptoms. In this work, patients with COVID-19 symptoms have been detected using eight distinct deep learning techniques, which are VGG16, InceptionResNetV2, ResNet50, DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2, using two datasets: one dataset includes 400 CT scan and another 400 chest X-ray images. Results show that NasNetMobile outperformed all other models by achieving an accuracy of 82.94% in CT scan and 93.94% in chest X-ray datasets. Besides, Local Interpretable Model-agnostic Explanations (LIME) is used. Results demonstrate that the proposed models can identify the infectious regions and top features; ultimately, it provides a potential opportunity to distinguish between COVID-19 patients with others.

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.

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