Investigation of Usability of Artificial Intelligence Semantic Video Processing Methods in Medicine

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dc.contributor.authorUcuzal, Hasan
dc.contributor.authorTunç, Zeynep
dc.contributor.authorGüldoğan, Emek
dc.date.accessioned2024-08-04T19:53:15Z
dc.date.available2024-08-04T19:53:15Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAim: The goal of this study is to produce user-friendly software for healthcare professionals with various approaches such as detection, identification, classification, and tracking of polyps contained in endoscopic images utilizing appropriate video/image processing techniques and CNN architecture. Material and Method: There were 345 photos in total in the study. These photographs are images depicting anatomical milestones, clinical findings, or gastrointestinal procedures in the digestive tract that have been documented and validated by medical specialists (skilled endoscopists). Each class has hundreds of images. The photos were downloaded from https://datasets.simula.no/kvasir, which is a free source for educational and research purposes. In the modeling phase, CNN and the Max-Margin object detection technique (MMOD), one of the deep neural network designs in the Dlib package, were employed. The data set was separated as 80% training and 20% test dataset using the simple cross-validation method (hold-out). Precision, recall, F1-score, average precision (AP), mean average precision (mAP), ideal localization recall precision (oLRP), mean optimal LRP (moLRP), and intersection over union (IoU) were used to evaluate model performance. Results: When the previously described steps were performed on the open-access video image dataset of endoscopic polyps in the current study, all performance metrics examined in the training dataset received a value of 1, whereas, in the test dataset precision, sensitivity, F1-score, AP, mAP, oLRP, and moLRP were 98%, 90%, 94%, 89%, 89%, 48%, and 48% respectively. Conclusion: The proposed approach was found to make accurate predictions in the diagnosis of gastrointestinal polyps based on the values of the calculated performance criteria.en_US
dc.identifier.doi10.37990/medr.1093272
dc.identifier.endpage303en_US
dc.identifier.issn2687-4555
dc.identifier.issue3en_US
dc.identifier.startpage297en_US
dc.identifier.trdizinid1126918en_US
dc.identifier.urihttps://doi.org/10.37990/medr.1093272
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1126918
dc.identifier.urihttps://hdl.handle.net/11616/89606
dc.identifier.volume4en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofMedical records-international medical journal (Online)en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleInvestigation of Usability of Artificial Intelligence Semantic Video Processing Methods in Medicineen_US
dc.typeArticleen_US

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