首页|New Study Findings from Catholic University in Erbil Illuminate Research in Mach ine Learning (Design and development of an effective classifier for medical imag es based on machine learning and image segmentation)
New Study Findings from Catholic University in Erbil Illuminate Research in Mach ine Learning (Design and development of an effective classifier for medical imag es based on machine learning and image segmentation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting from Catholic University in Erbil by NewsRx journalists, research stated, "Recently, there has been an incr ease in the death rate due to encephaloma tumours affecting all age groups." Our news editors obtained a quote from the research from Catholic University in Erbil: "Because of their intricate designs and the interference they cause in di agnostic imaging, these tumours are notoriously difficult to spot. Early and acc urate detection of tumours is crucial because it allows for identifying and pred icting malignant regions using medical imaging. Using segmentation and relegatio n techniques, medical scans can aid clinicians in making an early diagnosis and potentially save time. On the other hand, the identification of tumours may be a laborious and extended process for professional doctors owing to the complex na ture of tumour formations and the presence of noise in the data produced by Magn etic Resonance Imaging (MRI) since it is pretty imperative to locate and determi ne the site of the tumour as quickly as feasible. This research proposes a metho d for detecting brain cancers from MRI scans based on machine learning. It uses the Support Vector Machine, K Nearest Neighbor, and Nave Bayes algorithms for im age preprocessing, picture segmentation, feature extraction, and classification. " According to the news editors, the research concluded: "According to the finding s, the SVM algorithm accomplished the best level of accuracy, which is 89 % ."
Catholic University in ErbilCyborgsE merging TechnologiesMachine Learning
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