Auto-detection of necessity for MRI-guided online adaptive replanning using a machine learning classifier.
Sign Up to like & getrecommendations! Published in 2022 at "Medical physics"
DOI: 10.1002/mp.16047
Abstract: PURPOSE MRI-guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however it can be labor-intensive and time-consuming. Currently, the decision to perform OLAR for a treatment fraction… read more here.
Keywords: learning classifier; machine; machine learning; daily mri ... See more keywords
Screening Glaucoma With Red-free Fundus Photography Using Deep Learning Classifier and Polar Transformation
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DOI: 10.1097/ijg.0000000000001187
Abstract: Précis: The novel proposed algorithm using deep learning classifier and polar transformation technique can be an economical as well as an effective tool for early detection of glaucomatous RNFL defect. Purpose: The main purpose of… read more here.
Keywords: rnfl defect; classifier polar; using deep; learning classifier ... See more keywords
Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier
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DOI: 10.1109/access.2021.3053917
Abstract: At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number… read more here.
Keywords: sentence level; fuzzy deep; rate; learning classifier ... See more keywords
Theoretical Analysis of Accuracy-Based Fitness on Learning Classifier Systems
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DOI: 10.1109/access.2022.3183618
Abstract: Like most evolutionary algorithms, accuracy-based learning classifier systems (XCSs) use a fitness metric to recognize the superiority of rules, under a principle that a higher-quality rule has a higher fitness. However, XCS must learn the… read more here.
Keywords: classifier systems; learning classifier; accuracy; based fitness ... See more keywords
Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis
Sign Up to like & getrecommendations! Published in 2021 at "Frontiers in Oncology"
DOI: 10.3389/fonc.2021.638262
Abstract: Objectives To differentiate Glioblastomas (GBM) and Brain Metastases (BM) using a radiomic features-based Machine Learning (ML) classifier trained from post-contrast three-dimensional T1-weighted (post-contrast 3DT1) MR imaging, and compare its performance in medical diagnosis versus human… read more here.
Keywords: learning classifier; radiomic features; machine learning; post contrast ... See more keywords