{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T05:10:56Z","timestamp":1779340256109,"version":"3.51.4"},"reference-count":90,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Plenty of disease types exist in world communities that can be explained by humans\u2019 lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.<\/jats:p>","DOI":"10.3390\/bdcc6010029","type":"journal-article","created":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T12:35:37Z","timestamp":1646742937000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2018-6637","authenticated-orcid":false,"given":"Maha","family":"Gharaibeh","sequence":"first","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dalia","family":"Alzu\u2019bi","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5200-1959","authenticated-orcid":false,"given":"Malak","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ismail","family":"Hmeidi","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Rustom","family":"Al Nasar","sequence":"additional","affiliation":[{"name":"Department of Information Technology, School of Engineering & Technology, ALDAR University College, Garhoud, Dubai 35529, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2203-4549","authenticated-orcid":false,"given":"Laith","family":"Abualigah","sequence":"additional","affiliation":[{"name":"Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan"},{"name":"School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2798-0104","authenticated-orcid":false,"given":"Amir H.","family":"Gandomi","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat. 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