首页|Findings in Machine Learning Reported from King Khalid University (Machine learn ing framework for simulation of artifacts in paranasal sinuses diagnosis using C T images)
Findings in Machine Learning Reported from King Khalid University (Machine learn ing framework for simulation of artifacts in paranasal sinuses diagnosis using C T images)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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 originating from Abha, Saudi Arabia, by NewsRx editors, the research stated, "In the medical field, diagnostic tools th at make use of deep neural networks have reached a level of performance never be fore seen. A proper diagnosis of a patient's condition is crucial in modern medi cine since it determines whether or not the patient will receive the care they n eed." Our news journalists obtained a quote from the research from King Khalid Univers ity: "Data from a sinus CT scan is uploaded to a computer and displayed on a hig h-definition monitor to give the surgeon a clear anatomical orientation before e ndoscopic sinus surgery. In this study, a unique method is presented for detecti ng and diagnosing paranasal sinus disorders using machine learning. The research ers behind the current study designed their own approach. To speed up diagnosis, one of the primary goals of our study is to create an algorithm that can accura tely evaluate the paranasal sinuses in CT scans. The proposed technology makes i t feasible to automatically cut down on the number of CT scan images that requir e investigators to manually search through them all. In addition, the approach o ffers an automatic segmentation that may be used to locate the paranasal sinus r egion and crop it accordingly. As a result, the suggested method dramatically re duces the amount of data that is necessary during the training phase. As a resul t, this results in an increase in the efficiency of the computer while retaining a high degree of performance accuracy."
King Khalid UniversityAbhaSaudi Arab iaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
