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المؤلفTawsifur, Rahman
المؤلفKhandakar, Amith
المؤلفQiblawey, Yazan
المؤلفTahir, Anas
المؤلفKiranyaz, Serkan
المؤلفAbul Kashem, Saad Bin
المؤلفIslam, Mohammad Tariqul
المؤلفAl Maadeed, Somaya
المؤلفZughaier, Susu M.
المؤلفKhan, Muhammad Salman
المؤلفChowdhury, Muhammad E.H.
تاريخ الإتاحة2021-05-19T09:15:40Z
تاريخ النشر2021-05-31
اسم المنشورComputers in Biology and Medicine
المعرّفhttp://dx.doi.org/10.1016/j.compbiomed.2021.104319
الاقتباسRahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S. B. A., ... & Chowdhury, M. E. (2021). Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Computers in biology and medicine, 132, 104319.
الرقم المعياري الدولي للكتاب00104825
معرّف المصادر الموحدhttps://www.sciencedirect.com/science/article/pii/S001048252100113X
معرّف المصادر الموحدhttp://hdl.handle.net/10576/18416
الملخصComputer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.
راعي المشروعCOVID19 Emergency Response Grant #QUERG-CENG-2020-1 from Qatar University, Doha, Qatar provided the support for the work and the claims made herein are solely the responsibility of the authors.
اللغةen
الناشرElsevier
الموضوعCOVID-19
Image enhancement
Chest X-ray images
Convolutional neural networks
Lung segmentation
العنوانExploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images
النوعArticle
رقم المجلد132
dc.accessType Open Access


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