TY  - JOUR
TI  - Investigation of Usability of Artificial Intelligence Semantic Video Processing Methods in Medicine
AB  - Aim: 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.
AU  - KÜÇÜKAKÇALI, ZEYNEP
AU  - Ucuzal, Hasan
AU  - GÜLDOĞAN, Emek
DO  - 10.37990/medr.1093272
PY  - 2022
JO  - Medical records-international medical journal (Online)
VL  - 4
IS  - 3
SN  - 2687-4555
SP  - 297
EP  - 303
DB  - TRDizin
UR  - http://search/yayin/detay/1126918
ER  -