{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T05:32:21Z","timestamp":1767850341530,"version":"3.49.0"},"reference-count":23,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004489","name":"Mitacs","doi-asserted-by":"publisher","award":["215457"],"award-info":[{"award-number":["215457"]}],"id":[{"id":"10.13039\/501100004489","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>In a nuclear power plant (NPP), the used tools are visually inspected to ensure their integrity before and after their use in the nuclear reactor. The manual inspection is usually performed by qualified technicians and takes a large amount of time (weeks up to months). In this work, we propose an automated tool inspection that uses a classification model for anomaly detection. The deep learning model classifies the computed tomography (CT) images as defective (with missing components) or defect-free. Moreover, the proposed algorithm enables incremental learning (IL) using a proposed thresholding technique to ensure a high prediction confidence by continuous online training of the deployed online anomaly detection model. The proposed algorithm is tested with existing state-of-the-art IL methods showing that it helps the model quickly learn the anomaly patterns. In addition, it enhances the classification model confidence while preserving a desired minimal performance.<\/jats:p>","DOI":"10.3390\/computation11070139","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T01:40:02Z","timestamp":1689039602000},"page":"139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Incremental Learning-Based Algorithm for Anomaly Detection Using Computed Tomography Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8495-5343","authenticated-orcid":false,"given":"Hossam A.","family":"Gabbar","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Applied Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2704-8528","authenticated-orcid":false,"given":"Oluwabukola Grace","family":"Adegboro","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4342-8341","authenticated-orcid":false,"given":"Abderrazak","family":"Chahid","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada"}]},{"given":"Jing","family":"Ren","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, S.Z., and Jain, A. (2009). Encyclopedia of Biometrics, Springer.","DOI":"10.1007\/978-0-387-73003-5"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual Lifelong Learning with Neural Networks: A Review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural Netw."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, Z., and Liu, B. (2018). Synthesis Lectures on Artificial Intelligence and Machine Learning, Springer.","DOI":"10.1007\/978-3-031-01581-6"},{"key":"ref_4","first-page":"3366","article-title":"A Continual Learning Survey: Defying Forgetting in Classification Tasks","volume":"44","author":"Delange","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Luo, Y., Yin, L., Bai, W., and Mao, K. (2020). An Appraisal of Incremental Learning Methods. Entropy, 22.","DOI":"10.3390\/e22111190"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without Forgetting","volume":"40","author":"Li","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1007\/978-3-030-01219-9_9","article-title":"Memory Aware Synapses: Learning What (not) to Forget","volume":"Volume 11207","author":"Ferrari","year":"2018","journal-title":"Proceedings of the Computer Vision\u2013ECCV 2018"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., and Grabska-Barwinska, A. (2017). Overcoming Catastrophic Forgetting in Neural Networks. arXiv.","DOI":"10.1073\/pnas.1611835114"},{"key":"ref_9","unstructured":"Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R. (2022). Progressive Neural Networks. arXiv."},{"key":"ref_10","unstructured":"Zhou, G., Sohn, K., and Lee, H. (2012, January 21). Online Incremental Feature Learning with Denoising Autoencoders. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, La Palma, Canary Islands."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mehrotra, K., Mohan, C., and Ranka, S. (1996). Elements of Artificial Neural Networks, The MIT Press.","DOI":"10.7551\/mitpress\/2687.001.0001"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014, January 1). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Belouadah, E., and Popescu, A. (2019, January 27). IL2M: Class Incremental Learning with Dual Memory. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00067"},{"key":"ref_15","first-page":"226","article-title":"Continual Domain Incremental Learning for Chest X-ray Classification in Low-Resource Clinical Settings","volume":"Volume 12968","author":"Srivastava","year":"2021","journal-title":"Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gabbar, H.A., Chahid, A., Khan, M.J.A., Adegboro, O.G., and Samson, M.I. (2022). CTIMS: Automated Defect Detection Framework Using Computed Tomography. Appl. Sci., 12.","DOI":"10.3390\/app12042175"},{"key":"ref_17","unstructured":"Belouadah, E. (2021). Large-Scale Deep Class-Incremental Learning. Computer Vision and Pattern Recognition [cs.CV]. [Thesis, Ecole Nationale Sup\u00e9rieure Mines-T\u00e9l\u00e9com Atlantique]."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1038\/s42256-022-00568-3","article-title":"Three Types of Incremental Learning","volume":"4","author":"Tuytelaars","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106420","DOI":"10.1016\/j.knosys.2020.106420","article-title":"Incremental Learning based Multi-Domain Adaptation for Object Detection","volume":"210","author":"Wei","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_20","unstructured":"Hsu, Y.-C., Liu, Y.-C., Ramasamy, A., and Kira, Z. (2019). Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines. arXiv."},{"key":"ref_21","unstructured":"(2023, July 02). Diondo X-ray Systems and Services. Available online: https:\/\/www.diondo.com\/."},{"key":"ref_22","unstructured":"(2023, July 02). New Vision Systems Canada Inc. Available online: https:\/\/nvscanada.ca\/."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Schimanek, R., Bilge, P., and Dietrich, F. (2023). Inspection in High-Mix and High-Throughput Handling with Skeptical and Incremental Learning. TechRxiv, techrxiv: 23284049.","DOI":"10.36227\/techrxiv.23284049"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/11\/7\/139\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:10:14Z","timestamp":1760127014000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/11\/7\/139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,10]]},"references-count":23,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["computation11070139"],"URL":"https:\/\/doi.org\/10.3390\/computation11070139","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,10]]}}}