首页|Ningbo No. 2 Hospital Researcher Discusses Research in Machine Learning (Enhanci ng the Diagnostic Accuracy of Sacroiliitis: A Machine Learning Approach Applied to Computed Tomography Imaging)
Ningbo No. 2 Hospital Researcher Discusses Research in Machine Learning (Enhanci ng the Diagnostic Accuracy of Sacroiliitis: A Machine Learning Approach Applied to Computed Tomography Imaging)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting out of Zhejiang, People’s Rep ublic of China, by NewsRx editors, research stated, “Aims/Background Sacroiliiti s is a challenging condition to diagnose accurately due to the subtle nature of its presentation in imaging studies.” Our news reporters obtained a quote from the research from Ningbo No. 2 Hospital : “This study aims to improve the diagnostic accuracy of sacroiliitis by applyin g advanced machine learning techniques to computed tomography (CT) images. We em ployed five convolutional neural network (CNN) models-Visual Geometry Group 16-l ayer Network (VGG16), ResNet101, DenseNet, Inception-v4, and ResNeXt-50-to analy ze a dataset of 830 CT images, including both sacroiliitis and non-sacroiliitis cases. Each model’s performance was evaluated using metrics such as accuracy, pr ecision, recall, F1 score, Receiver Operating Characteristic (ROC), and Area Und er the Curve (AUC). The interpretability of the models’ decisions was enhanced u sing Gradient-weighted Class Activation Mapping (Grad-CAM) visualization. The Re sNeXt-50 and Inception-v4 models demonstrated superior performance, achieving th e highest accuracy and F1 scores among the tested models. Grad-CAM visualization s offered insights into the decision-making processes, highlighting the models’ focus on relevant anatomical features critical for accurate diagnosis.”
Ningbo No. 2 HospitalZhejiangPeople’ s Republic of ChinaAsiaComputed TomographyCyborgsEmerging TechnologiesImaging TechnologyMachine LearningTechnology
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