{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T13:39:27Z","timestamp":1792503567042,"version":"4.5.0"},"reference-count":155,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T00:00:00Z","timestamp":1653091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models\u2014namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet\u2014were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests\u2014namely, the Mann\u2013Whitney test, paired t-test, and Wilcoxon test\u2014demonstrated its stability and reliability, with p &lt; 0.0001. The online system for each slice was &lt;1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.<\/jats:p>","DOI":"10.3390\/diagnostics12051283","type":"journal-article","created":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T09:18:08Z","timestamp":1653124688000},"page":"1283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans"],"prefix":"10.3390","volume":"12","author":[{"given":"Jasjit","family":"Suri","sequence":"first","affiliation":[{"name":"Stroke Diagnostic and Monitoring Division, AtheroPoint\u2122, Roseville, CA 95661, USA"},{"name":"Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-3886","authenticated-orcid":false,"given":"Sushant","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA"},{"name":"Department of Computer Science Engineering, PSIT, Kanpur 209305, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0005-5242","authenticated-orcid":false,"given":"Gian","family":"Chabert","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Carriero","sequence":"additional","affiliation":[{"name":"Department of Radiology, \u201cMaggiore della Carit\u00e0\u201d Hospital, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessio","family":"Pasch\u00e8","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pietro","family":"Danna","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Saba","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5528-8051","authenticated-orcid":false,"given":"Armin","family":"Mehmedovi\u0107","sequence":"additional","affiliation":[{"name":"University Hospital for Infectious Diseases, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0189-8612","authenticated-orcid":false,"given":"Gavino","family":"Faa","sequence":"additional","affiliation":[{"name":"Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inder","family":"Singh","sequence":"additional","affiliation":[{"name":"Stroke Diagnostic and Monitoring Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monika","family":"Turk","sequence":"additional","affiliation":[{"name":"The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paramjit","family":"Chadha","sequence":"additional","affiliation":[{"name":"Stroke Diagnostic and Monitoring Division, AtheroPoint\u2122, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amer","family":"Johri","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Cardiology, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Narendra","family":"Khanna","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Mavrogeni","sequence":"additional","affiliation":[{"name":"Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Laird","sequence":"additional","affiliation":[{"name":"Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gyan","family":"Pareek","sequence":"additional","affiliation":[{"name":"Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Miner","sequence":"additional","affiliation":[{"name":"Men\u2019s Health Center, Miriam Hospital, Providence, RI 02906, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0448-4765","authenticated-orcid":false,"given":"David","family":"Sobel","sequence":"additional","affiliation":[{"name":"Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Balestrieri","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petros","family":"Sfikakis","sequence":"additional","affiliation":[{"name":"Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5043-7962","authenticated-orcid":false,"given":"George","family":"Tsoulfas","sequence":"additional","affiliation":[{"name":"Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3825-532X","authenticated-orcid":false,"given":"Athanasios","family":"Protogerou","sequence":"additional","affiliation":[{"name":"Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Durga","family":"Misra","sequence":"additional","affiliation":[{"name":"Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4508-1233","authenticated-orcid":false,"given":"Vikas","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Kitas","sequence":"additional","affiliation":[{"name":"Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK"},{"name":"Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagjit","family":"Teji","sequence":"additional","affiliation":[{"name":"Ann and Robert H. Lurie Children\u2019s Hospital of Chicago, Chicago, IL 60611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mustafa","family":"Al-Maini","sequence":"additional","affiliation":[{"name":"Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Surinder","family":"Dhanjil","sequence":"additional","affiliation":[{"name":"AtheroPoint LLC, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Nicolaides","sequence":"additional","affiliation":[{"name":"Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2408, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aditya","family":"Sharma","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay","family":"Rathore","sequence":"additional","affiliation":[{"name":"AtheroPoint LLC, Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6603-9077","authenticated-orcid":false,"given":"Mostafa","family":"Fatemi","sequence":"additional","affiliation":[{"name":"Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7658-1572","authenticated-orcid":false,"given":"Azra","family":"Alizad","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pudukode","family":"Krishnan","sequence":"additional","affiliation":[{"name":"Neurology Department, Fortis Hospital, Bangalore 560076, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3512-5850","authenticated-orcid":false,"given":"Ferenc","family":"Nagy","sequence":"additional","affiliation":[{"name":"Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoltan","family":"Ruzsa","sequence":"additional","affiliation":[{"name":"Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1790-8640","authenticated-orcid":false,"given":"Mostafa","family":"Fouda","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8544-8397","authenticated-orcid":false,"given":"Subbaram","family":"Naidu","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5927-3201","authenticated-orcid":false,"given":"Klaudija","family":"Viskovic","sequence":"additional","affiliation":[{"name":"University Hospital for Infectious Diseases, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manudeep","family":"Kalra","sequence":"additional","affiliation":[{"name":"Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,21]]},"reference":[{"key":"ref_1","unstructured":"(2022, January 24). WHO Coronavirus (COVID-19) Dashboard. Available online: https:\/\/covid19.who.int\/."},{"key":"ref_2","first-page":"12609","article-title":"Molecular pathways triggered by COVID-19 in different organs: ACE2 receptor-expressing cells under attack? A review","volume":"24","author":"Saba","year":"2020","journal-title":"Eur. Rev. Med. Pharmacol. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1007\/s10554-020-02089-9","article-title":"Imaging in COVID-19-related myocardial injury","volume":"37","author":"Cau","year":"2021","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"215","DOI":"10.4239\/wjd.v12.i3.215","article-title":"Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review","volume":"12","author":"Viswanathan","year":"2021","journal-title":"World J. Diabetes"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.clinimag.2021.05.016","article-title":"Complications in COVID-19 patients: Characteristics of pulmonary embolism","volume":"77","author":"Cau","year":"2021","journal-title":"Clin. Imaging"},{"key":"ref_6","first-page":"5063","article-title":"Vaccine-induced severe thrombotic thrombocytopenia following COVID-19 vaccination: A report of an autoptic case and review of the literature","volume":"25","author":"Fanni","year":"2021","journal-title":"Eur. Rev. Med. Pharmacol. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1101\/gr.6.10.995","article-title":"A novel method for real time quantitative RT-PCR","volume":"6","author":"Gibson","year":"1996","journal-title":"Genome Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1677\/jme.1.01755","article-title":"Quantitative real-time RT-PCR\u2013A perspective","volume":"34","author":"Bustin","year":"2005","journal-title":"J. Mol. Endocrinol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"E115","DOI":"10.1148\/radiol.2020200432","article-title":"Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR","volume":"296","author":"Fang","year":"2020","journal-title":"Radiology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.pbiomolbio.2006.07.026","article-title":"What is ultrasound?","volume":"93","author":"Leighton","year":"2007","journal-title":"Prog. Biophys. Mol. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.clinimag.2020.04.001","article-title":"Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review","volume":"64","author":"Jacobi","year":"2020","journal-title":"Clin. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1109\/TMI.2005.862753","article-title":"Computer analysis of computed tomography scans of the lung: A survey","volume":"25","author":"Sluimer","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1007\/s11547-020-01269-w","article-title":"Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab","volume":"125","author":"Giannitto","year":"2020","journal-title":"Radiol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cau, R., Falaschi, Z., Pasche, A., Danna, P., Arioli, R., Arru, C.D., Zagaria, D., Tricca, S., Suri, J.S., and Karla, M.K. (2021). Computed tomography findings of COVID-19 pneumonia in Intensive Care Unit-patients. J. Public Health Res., 10.","DOI":"10.4081\/jphr.2021.2270"},{"key":"ref_15","unstructured":"Saba, L., and Suri, J.S. (2013). Multi-Detector CT Imaging: Principles, Head, Neck, and Vascular Systems, CRC Press."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e200196","DOI":"10.1148\/ryct.2020200196","article-title":"Accuracy and Reproducibility of Low-Dose Submillisievert Chest CT for the Diagnosis of COVID-19","volume":"2","author":"Dangis","year":"2020","journal-title":"Radiol. Cardiothorac. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2730","DOI":"10.1016\/j.ajem.2020.04.016","article-title":"Chest CT in patients suspected of COVID-19 infection: A reliable alternative for RT-PCR","volume":"38","author":"Majidi","year":"2020","journal-title":"Am. J. Emerg. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1148\/radiol.2020200230","article-title":"CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)","volume":"295","author":"Chung","year":"2020","journal-title":"Radiology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109041","DOI":"10.1016\/j.ejrad.2020.109041","article-title":"Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study","volume":"128","author":"Wu","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.irbm.2020.05.003","article-title":"Deep transfer learning based classification model for COVID-19 disease","volume":"43","author":"Pathak","year":"2020","journal-title":"Irbm"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"87","DOI":"10.2214\/AJR.20.23034","article-title":"Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients","volume":"215","author":"Salehi","year":"2020","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s11604-021-01120-w","article-title":"Ground-glass opacity (GGO): A review of the differential diagnosis in the era of COVID-19","volume":"39","author":"Cozzi","year":"2021","journal-title":"Jpn. J. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"E41","DOI":"10.1148\/radiol.2020200343","article-title":"Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing","volume":"296","author":"Xie","year":"2020","journal-title":"Radiology"},{"key":"ref_24","unstructured":"Gozes, O., Frid-Adar, M., Greenspan, H., Browning, P.D., Zhang, H., Ji, W., Bernheim, A., and Siegel, E. (2020). Rapid ai development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s11548-020-02286-w","article-title":"Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans","volume":"16","author":"Shalbaf","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_26","unstructured":"Yang, X., He, X., Zhao, J., Zhang, Y., Zhang, S., and Xie, P. (2020). COVID-CT-dataset: A CT scan dataset about COVID-19. arXiv, 490."},{"key":"ref_27","first-page":"1","article-title":"COVID-2019 detection using x-ray images and artificial intelligence hybrid systems","volume":"2","author":"Alqudah","year":"2020","journal-title":"Poitiers"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106912","DOI":"10.1016\/j.asoc.2020.106912","article-title":"CNN-based transfer learning\u2013BiLSTM network: A novel approach for COVID-19 infection detection","volume":"98","author":"Aslan","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1109\/TIP.2021.3058783","article-title":"Jcs: An explainable COVID-19 diagnosis system by joint classification and segmentation","volume":"30","author":"Wu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","article-title":"A deep learning system to screen novel coronavirus disease 2019 pneumonia","volume":"6","author":"Xu","year":"2020","journal-title":"Engineering"},{"key":"ref_31","unstructured":"Winston, P.H. (1992). Artificial Intelligence, Addison-Wesley Longman Publishing Co., Inc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"S36","DOI":"10.1016\/j.metabol.2017.01.011","article-title":"Artificial intelligence in medicine","volume":"69","author":"Hamet","year":"2017","journal-title":"Metabolism"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1308\/147870804290","article-title":"Artificial intelligence in medicine","volume":"86","author":"Ramesh","year":"2004","journal-title":"Ann. R. Coll. Surg. Engl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1007\/s10916-010-9645-2","article-title":"Symptomatic vs. asymptomatic plaque classification in carotid ultrasound","volume":"36","author":"Acharya","year":"2012","journal-title":"J. Med. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1177\/0954411913483637","article-title":"Diagnosis of Hashimoto\u2019s thyroiditis in ultrasound using tissue characterization and pixel classification","volume":"227","author":"Acharya","year":"2013","journal-title":"Proc. Inst. Mech. Eng. Part H J. Eng. Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.cmpb.2012.09.008","article-title":"Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization","volume":"110","author":"Acharya","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Acharya, U.R., Faust, O., Sree, S.V., Alvin, A.P.C., Krishnamurthi, G., Sanches, J., and Suri, J.S. (September, January 30). Atheromatic\u2122: Symptomatic vs. asymptomatic classification of carotid ultrasound plaque using a combination of HOS, DWT & texture. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091113"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s11517-012-1019-0","article-title":"Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: A paradigm for stroke risk assessment","volume":"51","author":"Acharya","year":"2013","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Acharya, U.R., Saba, L., Molinari, F., Guerriero, S., and Suri, J.S. (September, January 28). Ovarian tumor characterization and classification: A class of GyneScan\u2122 systems. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6346953"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.cmpb.2017.12.016","article-title":"Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm","volume":"155","author":"Biswas","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Molinari, F., Liboni, W., Pavanelli, E., Giustetto, P., Badalamenti, S., and Suri, J.S. (2007, January 22\u201326). Accurate and automatic carotid plaque characterization in contrast enhanced 2-D ultrasound images. Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4352292"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"545","DOI":"10.7785\/tcrt.2012.500346","article-title":"Prostate tissue characterization\/classification in 144 patient population using wavelet and higher order spectra features from transrectal ultrasound images","volume":"12","author":"Pareek","year":"2013","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.21037\/atm-20-7676","article-title":"Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: A narrative review for stroke application","volume":"9","author":"Saba","year":"2021","journal-title":"Ann. Transl. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s11517-021-02322-0","article-title":"Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: A supercomputer application","volume":"59","author":"Agarwal","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"439","DOI":"10.21037\/cdt.2019.09.01","article-title":"Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: A deep learning paradigm","volume":"9","author":"Saba","year":"2019","journal-title":"Cardiovasc. Diagn. Ther."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s11517-018-1897-x","article-title":"Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: A tool for stroke risk","volume":"57","author":"Biswas","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","article-title":"Deep learning techniques for medical image segmentation: Achievements and challenges","volume":"32","author":"Hesamian","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep learning applications in medical image analysis","volume":"6","author":"Ker","year":"2017","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"Razzak, M.I., Naz, S., and Zaib, A. (2018). Classification in BioApps, Springer."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"100004","DOI":"10.1016\/j.array.2019.100004","article-title":"A review: Deep learning for medical image segmentation using multi-modality fusion","volume":"3","author":"Zhou","year":"2019","journal-title":"Array"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.jormas.2019.06.002","article-title":"Deep learning in medical image analysis: A third eye for doctors","volume":"120","author":"Fourcade","year":"2019","journal-title":"J. Stomatol. Oral Maxillofac. Surg."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4128","DOI":"10.1109\/JBHI.2021.3103839","article-title":"Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective","volume":"25","author":"Suri","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"104721","DOI":"10.1016\/j.compbiomed.2021.104721","article-title":"Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound","volume":"136","author":"Jain","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"104803","DOI":"10.1016\/j.compbiomed.2021.104803","article-title":"Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review","volume":"137","author":"Jena","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_57","unstructured":"Suri, J.S. (2011). Imaging Based Symptomatic Classification and Cardiovascular Stroke Risk Score Estimation. (2011\/0257545 A1), U.S. Patent."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Agarwal, S., Elavarthi, P., Pathak, R., Ketireddy, V., Columbu, M., Saba, L., Gupta, S.K., Faa, G., and Singh, I.M. (2021). Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography. Diagnostics, 11.","DOI":"10.3390\/diagnostics11112025"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Suri, J.S., Agarwal, S., Pathak, R., Ketireddy, V., Columbu, M., Saba, L., Gupta, S.K., Faa, G., Singh, I.M., and Turk, M. (2021). COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081405"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s11548-021-02317-0","article-title":"Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs","volume":"16","author":"Saba","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1049\/el.2020.2102","article-title":"Brain MRI-based Wilson disease tissue classification: An optimised deep transfer learning approach","volume":"56","author":"Saba","year":"2020","journal-title":"Electron. Lett."},{"key":"ref_62","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1049\/el.2018.0989","article-title":"Horizon detection in maritime images using scene parsing network","volume":"54","author":"Jeong","year":"2018","journal-title":"Electron. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"751","DOI":"10.2174\/1573405616666201231100623","article-title":"An automatic glioma segmentation system using a multilevel attention pyramid scene parsing network","volume":"17","author":"Zhang","year":"2021","journal-title":"Curr. Med. Imaging"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_68","unstructured":"Mazaheri, G., Mithun, N.C., Bappy, J.H., and Roy-Chowdhury, A.K. (2019, January 16\u201320). A Skip Connection Architecture for Localization of Image Manipulations. Proceedings of the CVPR Workshops, Long Beach, CA, USA."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Liu, F., Ren, X., Zhang, Z., Sun, X., and Zou, Y. (2020, January 8\u201313). Rethinking skip connection with layer normalization. Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain.","DOI":"10.18653\/v1\/2020.coling-main.320"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Mateen, M., Wen, J., Song, S., and Huang, Z. (2018). Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry, 11.","DOI":"10.3390\/sym11010001"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wen, L., Li, X., Li, X., and Gao, L. (2019, January 6\u20138). A new transfer learning based on VGG-19 network for fault diagnosis. Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Porto, Portugal.","DOI":"10.1109\/CSCWD.2019.8791884"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Xiao, J., Wang, J., Cao, S., and Li, B. (2020, January 20\u201322). Application of a novel and improved VGG-19 network in the detection of workers wearing masks. Proceedings of the Journal of Physics: Conference Series, Sanya, China.","DOI":"10.1088\/1742-6596\/1518\/1\/012041"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"014502","DOI":"10.1117\/1.JMI.8.S1.014502","article-title":"Deep CNN models for predicting COVID-19 in CT and X-ray images","volume":"8","author":"Chaddad","year":"2021","journal-title":"J. Med. Imaging"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Karasawa, H., Liu, C.-L., and Ohwada, H. (2018, January 19\u201321). Deep 3d convolutional neural network architectures for alzheimer\u2019s disease diagnosis. Proceedings of the Asian Conference on Intelligent Information and Database Systems, Dong Hoi City, Vietnam.","DOI":"10.1007\/978-3-319-75417-8_27"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1109\/TIT.1981.1056373","article-title":"Properties of cross-entropy minimization","volume":"27","author":"Shore","year":"1981","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A tutorial on the cross-entropy method","volume":"134","author":"Kroese","year":"2005","journal-title":"Ann. Oper. Res."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Jamin, A., and Humeau-Heurtier, A. (2019). (Multiscale) cross-entropy methods: A review. Entropy, 22.","DOI":"10.3390\/e22010045"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1364\/BOE.449314","article-title":"Automatic geographic atrophy segmentation using optical attenuation in OCT scans with deep learning","volume":"13","author":"Chu","year":"2022","journal-title":"Biomed. Opt. Express"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Basar, S., Waheed, A., Ali, M., Zahid, S., Zareei, M., and Biswal, R.R. (2022). An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN. Sensors, 22.","DOI":"10.3390\/s22072724"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1109\/TMI.2020.3002417","article-title":"Optimization for medical image segmentation: Theory and practice when evaluating with dice score or jaccard index","volume":"39","author":"Eelbode","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1093\/clinchem\/48.5.799","article-title":"Application of the Bland\u2013Altman plot for interpretation of method-comparison studies: A critical investigation of its practice","volume":"48","author":"Dewitte","year":"2002","journal-title":"Clin. Chem."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"141","DOI":"10.11613\/BM.2015.015","article-title":"Understanding bland altman analysis","volume":"25","author":"Giavarina","year":"2015","journal-title":"Biochem. Med."},{"key":"ref_84","first-page":"307","article-title":"Receiver operating characteristic (ROC) methodology: The state of the art","volume":"29","author":"Hanley","year":"1989","journal-title":"Crit. Rev. Diagn. Imaging"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1093\/clinchem\/39.4.561","article-title":"Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine","volume":"39","author":"Zweig","year":"1993","journal-title":"Clin. Chem."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1177\/070674370705200210","article-title":"What\u2019s under the ROC? An introduction to receiver operating characteristics curves","volume":"52","author":"Streiner","year":"2007","journal-title":"Can. J. Psychiatry"},{"key":"ref_87","first-page":"184","article-title":"The differences and similarities between two-sample t-test and paired t-test","volume":"29","author":"Xu","year":"2017","journal-title":"Shanghai Arch. Psychiatry"},{"key":"ref_88","unstructured":"Hsu, H., and Lachenbruch, P.A. (2014). Wiley Statsref: Statistics Reference Online, Wiley Online Library."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"13","DOI":"10.20982\/tqmp.04.1.p013","article-title":"The Mann-Whitney U: A test for assessing whether two independent samples come from the same distribution","volume":"4","author":"Nachar","year":"2008","journal-title":"Tutor. Quant. Methods Psychol."},{"key":"ref_90","unstructured":"McKnight, P.E., and Najab, J. (2010). The Corsini Encyclopedia of Psychology, Wiley."},{"key":"ref_91","unstructured":"Birnbaum, Z. (1956). Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, 1956, University of California."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1002\/sim.4780040112","article-title":"A Wilcoxon-type test for trend","volume":"4","author":"Cuzick","year":"1985","journal-title":"Stat. Med."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0169-2607(95)01703-8","article-title":"MedCalc: A new computer program for medical statistics","volume":"48","author":"Schoonjans","year":"1995","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_94","unstructured":"MedCalc (2022, January 24). MedCalc Statistical Software. Available online: https:\/\/www.scirp.org\/(S(lz5mqp453ed%20snp55rrgjct55))\/reference\/referencespapers.aspx?referenceid=2690486."},{"key":"ref_95","unstructured":"Riffenburgh, R.H., and Gillen, D.L. (2020). Statistics in Medicine, Academic Press. [4th ed.]."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1007\/s10916-016-0504-7","article-title":"Inter-observer variability analysis of automatic lung delineation in normal and disease patients","volume":"40","author":"Saba","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_97","first-page":"15750","article-title":"Disentangling human error from ground truth in segmentation of medical images","volume":"33","author":"Zhang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Foncubierta Rodr\u00edguez, A., and M\u00fcller, H. (2012, January 29). Ground truth generation in medical imaging: A crowdsourcing-based iterative approach. Proceedings of the ACM Multimedia 2012 Workshop on Crowdsourcing for Multimedia, Online.","DOI":"10.1145\/2390803.2390808"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"e200034","DOI":"10.1148\/ryct.2020200034","article-title":"Imaging profile of the COVID-19 infection: Radiologic findings and literature review","volume":"2","author":"Ng","year":"2020","journal-title":"Radiol. Cardiothorac. Imaging"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"526","DOI":"10.3389\/fmed.2020.00526","article-title":"Pulmonary and extra-pulmonary clinical manifestations of COVID-19","volume":"7","author":"Johnson","year":"2020","journal-title":"Front. Med."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Mottaghi, A., Roham, M., Makiani, M.J., Ranjbar, M., Laali, A., and Rahimian, N.R. (2022, January 24). Verifying Extra-Pulmonary Manifestation of COVID-19 in Firoozgar Hospital 2020: An Observational Study. Available online: https:\/\/www.researchgate.net\/publication\/348593779_Verifying_Extra-Pulmonary_Manifestation_of_COVID-19_in_Firoozgar_Hospital_2020_An_Observational_Study.","DOI":"10.18502\/jimc.v5i3.10949"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.dsx.2020.03.013","article-title":"Cardiovascular disease and COVID-19","volume":"14","author":"Bansal","year":"2020","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1177\/2048872620974605","article-title":"Cardiac complications in patients hospitalised with COVID-19","volume":"9","author":"Linschoten","year":"2020","journal-title":"Eur. Heart J. Acute Cardiovasc. Care"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/17476348.2020.1787835","article-title":"COPD patients in a COVID-19 society: Depression and anxiety","volume":"15","author":"Yohannes","year":"2021","journal-title":"Expert Rev. Respir. Med."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"382","DOI":"10.15585\/mmwr.mm6913e2","article-title":"Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019\u2014United States, 12 February\u201328 March 2020","volume":"69","author":"Team","year":"2020","journal-title":"Morb. Mortal. Wkly. Rep."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"608525","DOI":"10.3389\/fmed.2020.608525","article-title":"COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images","volume":"7","author":"Gunraj","year":"2020","journal-title":"Front. Med."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"e368","DOI":"10.7717\/peerj-cs.368","article-title":"Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images","volume":"7","author":"Iyer","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"5544742","DOI":"10.1155\/2021\/5544742","article-title":"Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images","volume":"2021","author":"Ranjbarzadeh","year":"2021","journal-title":"BioMed Res. Int."},{"key":"ref_109","unstructured":"Ding, W., Abdel-Basset, M., Hawash, H., and Elkomy, O.M. (2021). MT-nCov-Net: A Multitask Deep-Learning Framework for Efficient Diagnosis of COVID-19 Using Tomography Scans. IEEE Trans. Cybern."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"5208940","DOI":"10.1155\/2021\/5208940","article-title":"A Study on Weak Edge Detection of COVID-19\u2032s CT Images Based on Histogram Equalization and Improved Canny Algorithm","volume":"2021","author":"Hou","year":"2021","journal-title":"Comput. Math Methods Med."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Kumar Singh, V., Abdel-Nasser, M., Pandey, N., and Puig, D. (2021). LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework. Diagnostics, 11.","DOI":"10.3390\/diagnostics11020158"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Lizzi, F., Agosti, A., Brero, F., Cabini, R.F., Fantacci, M.E., Figini, S., Lascialfari, A., Laruina, F., Oliva, P., and Piffer, S. (2021). Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: Training and assessment on multipledatasets using different annotation criteria. arXiv.","DOI":"10.1007\/s11548-021-02501-2"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"106406","DOI":"10.1016\/j.cmpb.2021.106406","article-title":"DR-MIL: Deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images","volume":"211","author":"Qi","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1109\/TNNLS.2021.3054746","article-title":"Anam-Net: Anamorphic depth embedding-based lightweight CNN for segmentation of anomalies in COVID-19 chest CT images","volume":"32","author":"Paluru","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Zhang, P., Zhong, Y., Deng, Y., Tang, X., and Li, X. (2020). CoSinGAN: Learning COVID-19 Infection Segmentation from a Single Radiological Image. Diagnostics, 10.","DOI":"10.3390\/diagnostics10110901"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s10278-021-00434-5","article-title":"Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism","volume":"34","author":"Budak","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"104037","DOI":"10.1016\/j.compbiomed.2020.104037","article-title":"Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation","volume":"126","author":"Amyar","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1016\/j.acra.2020.09.004","article-title":"CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients","volume":"27","author":"Cai","year":"2020","journal-title":"Acad. Radiol."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1002\/mp.14676","article-title":"Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation","volume":"48","author":"Ma","year":"2021","journal-title":"Med. Phys."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"9161","DOI":"10.1007\/s11042-020-10010-8","article-title":"AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans","volume":"80","author":"Kuchana","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2net: A new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/S0031-3203(00)00023-6","article-title":"On the Canny edge detector","volume":"34","author":"Ding","year":"2001","journal-title":"Pattern Recognit."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s11263-010-0392-0","article-title":"The Canny edge detector revisited","volume":"91","author":"McIlhagga","year":"2011","journal-title":"Int. J. Comput. Vis."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","article-title":"Inf-net: Automatic COVID-19 lung infection segmentation from ct images","volume":"39","author":"Fan","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"M\u00fcller, D., and Kramer, F. (2021). MIScnn: A framework for medical image segmentation with convolutional neural networks and deep learning. BMC Med. Imaging, 21.","DOI":"10.1186\/s12880-020-00543-7"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"M\u00fcller, D., Rey, I.S., and Kramer, F. (2020). Automated chest ct image segmentation of COVID-19 lung infection based on 3d u-net. arXiv.","DOI":"10.1016\/j.imu.2021.100681"},{"key":"ref_129","unstructured":"Arunachalam, A., Ravi, V., Acharya, V., and Pham, T.D. (2021). Toward data-model-agnostic autonomous machine-generated data labeling and annotation platform: COVID-19 autoannotation use case. IEEE Trans. Eng. Manag., 1\u201312."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1088\/0031-9155\/41\/1\/009","article-title":"The calibration of CT Hounsfield units for radiotherapy treatment planning","volume":"41","author":"Schneider","year":"1996","journal-title":"Phys. Med. Biol."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1007\/s00066-013-0464-5","article-title":"Hounsfield units variations","volume":"190","author":"Zurl","year":"2014","journal-title":"Strahlenther. Onkol."},{"key":"ref_132","unstructured":"DenOtter, T.D., and Schubert, J. (2019). Hounsfield Unit, StatPearls Publishing."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1137\/0733060","article-title":"Multiresolution representation of data: A general framework","volume":"33","author":"Harten","year":"1996","journal-title":"SIAM J. Numer. Anal."},{"key":"ref_134","unstructured":"Rosenfeld, A. (2013). Multiresolution Image Processing and Analysis, Springer Science & Business Media."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/TIP.2011.2169270","article-title":"Completely automated multiresolution edge snapper\u2014A new technique for an accurate carotid ultrasound IMT measurement: Clinical validation and benchmarking on a multi-institutional database","volume":"21","author":"Molinari","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"El-Baz, A., Gimel\u2019farb, G., and Suri, J.S. (2015). Stochastic Modeling for Medical Image Analysis, CRC Press. [1st ed.].","DOI":"10.1201\/b19253"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"El-Baz, A., and Suri, J.S. (2019). Big Data in Multimodal Medical Imaging, CRC Press.","DOI":"10.1201\/b22410"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.ihj.2018.01.024","article-title":"Intra- and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement","volume":"70","author":"Saba","year":"2018","journal-title":"Indian Heart J."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.compbiomed.2017.08.014","article-title":"Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework","volume":"89","author":"Than","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_140","first-page":"384","article-title":"Inter-and Intra-Observer Variability Assessment of in Vivo Carotid Plaque Burden Quantification Using Multi-Contrast Dark Blood MR Images","volume":"113","author":"Zhang","year":"2005","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_141","first-page":"395","article-title":"Appearance analysis for the early assessment of detected lung nodules","volume":"17","author":"Gimelfarb","year":"2011","journal-title":"Lung Imaging Comput. Aided Diagn."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-015-0214-6","article-title":"Automatic lung segmentation using control feedback system: Morphology and texture paradigm","volume":"39","author":"Noor","year":"2015","journal-title":"J. Med. Syst."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Sanagala, S.S., Nicolaides, A., Gupta, S.K., Koppula, V.K., Saba, L., Agarwal, S., Johri, A.M., Kalra, M.S., and Suri, J.S. (2021). Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification. Diagnostics, 11.","DOI":"10.3390\/diagnostics11112109"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1016\/j.cmpb.2012.05.008","article-title":"Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods","volume":"108","author":"Molinari","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Shu, X., Yang, J., Yan, R., and Song, Y. (2022). Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition. IEEE Trans. Circuits Syst. Video Technol., 1.","DOI":"10.1109\/TCSVT.2022.3142771"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"3300","DOI":"10.1109\/TPAMI.2021.3050918","article-title":"Spatiotemporal co-attention recurrent neural networks for human-skeleton motion prediction","volume":"44","author":"Shu","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Shu, X., Qi, G.-J., Tang, J., and Wang, J. (2015, January 13). Weakly-shared deep transfer networks for heterogeneous-domain knowledge propagation. Proceedings of the 23rd ACM International Conference on Multimedia, Online.","DOI":"10.1145\/2733373.2806216"},{"key":"ref_148","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., and Jorge Cardoso, M. (2017). Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer."},{"key":"ref_149","unstructured":"Xu, J., Zhang, Z., Friedman, T., Liang, Y., and Broeck, G. (2018, January 26\u201328). A semantic loss function for deep learning with symbolic knowledge. Proceedings of the International Conference on Machine Learning, Macau, China."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1007\/s00521-015-1916-x","article-title":"Efficient and robust deep learning with correntropy-induced loss function","volume":"27","author":"Chen","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_151","unstructured":"Salehi, S.S.M., Erdogmus, D., and Gholipour, A. (2017). Machine Learning in Medical Imaging, Springer."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.patrec.2017.05.018","article-title":"Weighted kappa loss function for multi-class classification of ordinal data in deep learning","volume":"105","author":"Puig","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_153","first-page":"9","article-title":"Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: An asymptomatic Japanese cohort study","volume":"41","author":"Jain","year":"2022","journal-title":"Int. Angiol. A J. Int. Union Angiol."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"919","DOI":"10.21037\/cdt.2020.01.07","article-title":"Cardiovascular\/stroke risk predictive calculators: A comparison between statistical and machine learning models","volume":"10","author":"Jamthikar","year":"2020","journal-title":"Cardiovasc. Diagn. Ther."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"103804","DOI":"10.1016\/j.compbiomed.2020.103804","article-title":"Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm","volume":"122","author":"Tandel","year":"2020","journal-title":"Comput. Biol. Med."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/5\/1283\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:16:12Z","timestamp":1760138172000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/5\/1283"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,21]]},"references-count":155,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["diagnostics12051283"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics12051283","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,21]]}}}