首页|Hanoi University of Science and Technology Reports Findings in Machine Learning (Comparing the accuracy of two machine learning models in detection and classifi cation of periapical lesions using periapical radiographs)
Hanoi University of Science and Technology Reports Findings in Machine Learning (Comparing the accuracy of two machine learning models in detection and classifi cation of periapical lesions using periapical radiographs)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Hanoi, Vietnam, by NewsR x journalists, research stated, "Previous deep learning-based studies were mainl y conducted on detecting periapical lesions; limited information in classificati on, such as the periapical index (PAI) scoring system, is available. The study a imed to apply two deep learning models, Faster R-CNN and YOLOv4, in detecting an d classifying periapical lesions using the PAI score from periapical radiographs (PR) in three different regions of the dental arch: anterior teeth, premolars, and molars." The news correspondents obtained a quote from the research from the Hanoi Univer sity of Science and Technology, "Out of 2658 PR selected for the study, 2122 PR were used for training, 268 PR were used for validation and 268 PR were used for testing. The diagnosis made by experienced dentists was used as the reference d iagnosis. The Faster R-CNN and YOLOv4 models obtained great sensitivity, specifi city, accuracy, and precision for detecting periapical lesions. No clear differe nce in the performance of both models among these three regions was found. The t rue prediction of Faster R-CNN was 89%, 83.01% and 91 .84% for PAI 3, PAI 4 and PAI 5 lesions, respectively. The corresp onding values of YOLOv4 were 68.06%, 50.94%, and 65.31 %."
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