首页|Data on Machine Learning Reported by Naimul Khan and Colleagues (Diagnosis of pl acenta accreta spectrum using ultrasound texture feature fusion and machine lear ning)
Data on Machine Learning Reported by Naimul Khan and Colleagues (Diagnosis of pl acenta accreta spectrum using ultrasound texture feature fusion and machine lear ning)
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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 originating from Toronto, Can ada, by NewsRx correspondents, research stated, “Placenta accreta spectrum (PAS) is an obstetric disorder arising from the abnormal adherence of the placenta to the uterine wall, often leading to life-threatening complications including pos tpartum hemorrhage. Despite its significance, PAS remains frequently underdiagno sed before delivery.” Our news editors obtained a quote from the research, “This study delves into the realm of machine learning to enhance the precision of PAS classification. We in troduce two distinct models for PAS classification employing ultrasound texture features. The first model leverages machine learning techniques, harnessing text ure features extracted from ultrasound scans. The second model adopts a linear c lassifier, utilizing integrated features derived from ‘weighted z-scores’. A nov el aspect of our approach is the amalgamation of classical machine learning and statistical-based methods for feature selection. This, coupled with a more trans parent classification model based on quantitative image features, results in sup erior performance compared to conventional machine learning approaches. Our line ar classifier and machine learning models attain test accuracies of 87 % and 92 %, and 5-fold cross validation accuracies of 88.7 (4.4) and 83.0 (5.0), respectively. The proposed models illustrate the effectiveness of pr actical and robust tools for enhanced PAS detection, offering non-invasive and c omputationally-efficient diagnostic tools.”
TorontoCanadaNorth and Central Ameri caCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and Medici neMachine Learning
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