Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images
By Tawsifur Rahman, Amith Abdullah Khandakar, Yazan Qiblawey, A. Tahir, Serkan Kiranyaz, S. Kashem, Mohammad Tariqul Islam, S. Maadeed, S. Zughaier, Muhammad Salman Khan and 1 more. Comput. Biol. Medicine, 2021
1,001 citations recorded by the source corpus.
Computer-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.
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/466f2f351ee53ae9876682f5dc7c2158b3c81424
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In Artificial Intelligence
Citations in the corpus
- AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection (2023, neutral): “…2020), and Endo (Hicks et al., 2021), thyroid nodule detection dataset TN3k (Gong et al., 2021), brain tumor detection datasets HeadCT (Salehi et al., 2021), BrainMRI (Salehi et al., 2021), Br35H (Hamada., 2020), and COVID-19 detection dataset COVID-19 (Chowdhury et al., 2020; Rahman et al., 2021).”
- MedCLIP: Contrastive Learning from Unpaired Medical Images and Text (2022, neutral): “COVID (Rahman et al., 2021) is a publicly available x-ray dataset with COVID v.s. non-COVID labels.”
- Evaluation metrics and statistical tests for machine learning (2024, methodology): “The first data set contains 3000 chest X-rays of COVID-19 patients and 3000 chest X-rays of healthy patients chosen from COVID-19 Radiography Database 61,62 .”