Deep learning based detection and analysis of COVID-19 on chest X-ray images
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TLDR
The PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients are taken and deep learning-based CNN models are used, which give the highest accuracy for detecting Chest X-rays images as compared to other models.Abstract:
Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.read more
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Journal ArticleDOI
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images.
Tawsifur Rahman,Amith Khandakar,Yazan Qiblawey,Anas Tahir,Serkan Kiranyaz,Saad Bin Abul Kashem,Mohammad Tariqul Islam,Somaya Al Maadeed,Susu M. Zughaier,Muhammad Salman Khan,Muhammad E. H. Chowdhury +10 more
TL;DR: In this article, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature; however, the proposed approach with very reliable and comparable performance will boost the fast and robust detection of coronavirus disease using chest X-ray images.
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Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images
Tawsifur Rahman,Amith Khandakar,Yazan Qiblawey,Anas Tahir,Serkan Kiranyaz,Saad Bin Abul Kashem,Mohammad Tariqul Islam,Somaya Al Maadeed,Susu M. Zughaier,Muhammad Salman Khan,Muhammad E. H. Chowdhury +10 more
TL;DR: An approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images and the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique.
Journal ArticleDOI
Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network
Amit Kumar Das,Sayantani Ghosh,Samiruddin Thunder,Rohit Dutta,Sachin Agarwal,Amlan Chakrabarti +5 more
TL;DR: A Deep Convolutional Neural Network-based solution which can detect the COVID-19 +ve patients using chest X-Ray images using a classification accuracy higher than the state-of-the-art CNN models as well the compared benchmark algorithm is proposed.
Journal ArticleDOI
COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion.
Muhammad Attique Khan,Majed Alhaisoni,Usman Tariq,Nazar Hussain,Abdul Majid,Robertas Damaševičius,Rytis Maskeliūnas +6 more
TL;DR: In this article, a new automated technique is proposed using parallel fusion and optimization of deep learning models for case classification of COVID-19 case classification, which starts with a contrast enhancement using a combination of top-hat and Wiener filters, and features are extracted and fused using a parallel fusion approach-parallel positive correlation.
Journal ArticleDOI
Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images.
TL;DR: In this article, a large-scale learning with stacked ensemble meta-classifier and deep learning-based feature fusion approach for COVID-19 classification was proposed for point-of-care diagnosis by healthcare professionals.
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