Πλοήγηση ανά Θέμα "support vector machine"
Αποτελέσματα 1-13 από 13
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Artificial Intelligence in Cardiology—A Narrative Review of Current Status
(2022)Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical ... -
Breast Cancer Classification on Multiparametric MRI – Increased Performance of Boosting Ensemble Methods
(2022)Introduction: This study aims to assess the utility of Boosting ensemble classification methods for increasing the diagnostic performance of multiparametric Magnetic Resonance Imaging (mpMRI) radiomic models, in differentiating ... -
DINOSARC: Color Features Based on Selective Aggregation of Chromatic Image Components for Wireless Capsule Endoscopy
(2018)Wireless Capsule Endoscopy (WCE) is a noninvasive diagnostic technique enabling the inspection of the whole gastrointestinal (GI) tract by capturing and wirelessly transmitting thousands of color images. Proprietary software ... -
The effect of sorptivity on cumulative infiltration
(2021)Hydraulic parameters of the soil play a considerable role in the hydrological cycle, irrigation planning, drainage, groundwater recharge, and water resources management. One of the most important hydraulic parameters of ... -
An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data
(2022)Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and huge ... -
Imaging biomarker analysis of advanced multiparametric MRI for glioma grading
(2019)Aims and objectives: To investigate the value of advanced multiparametric MR imaging biomarker analysis based on radiomic features and machine learning classification, in the non-invasive evaluation of tumor heterogeneity ... -
Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review
(2022)The improved treatment of knee injuries critically relies on having an accurate and costeffective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim ... -
Machine learning approaches for predicting health risk of cyanobacterial blooms in Northern European Lakes
(2020)Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail ... -
Machine Learning in Meningioma MRI: Past to Present. A Narrative Review
(2022)Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it ... -
microTSS: Accurate microRNA transcription start site identification reveals a significant number of divergent pri-miRNAs
(2014)A large fraction of microRNAs (miRNAs) are derived from intergenic non-coding loci and the identification of their promoters remains 'elusive'. Here, we present microTSS, a machine-learning algorithm that provides highly ... -
Modelling of infiltration using artificial intelligence techniques in semi-arid Iran
(2019)Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector ... -
Multi-parametric MRI lesion heterogeneity biomarkers for breast cancer diagnosis
(2020)Purpose: To identify intra-lesion imaging heterogeneity biomarkers in multi-parametric Magnetic Resonance Imaging (mpMRI) for breast lesion diagnosis. Methods: Dynamic Contrast Enhanced (DCE) and Diffusion Weighted Imaging ... -
A two-stage method for microcalcification cluster segmentation in mammography by deformable models
(2015)Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature ...