Motor strength classification with machine learning approaches applied to anatomical neuroimages

被引：0

作者：

Bardozzo, Francesco ^{[1
]}

Uribe, Sebastian Cano ^{[1
,2
]}

Russo, Andrea G. ^{[3
]}

Castano, Mateo Jimenez ^{[1
,2
]}

Priscolli, Mattia Delli ^{[1
]}

Esposito, Fabrizio ^{[3
]}

Tagliaferri, Roberto ^{[1
]}

机构：

[1] Univ Salerno, DISA MIS, Neuronelab, Salerno, Italy

[2] Fac Engn UTP, Pereira, Colombia

[3] Univ Salerno, DIPMED, Salerno, Italy

来源：

2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2020年

关键词：

Pattern recognition; motor-strength; machine learning; classification; feature extraction; FEATURE-EXTRACTION; INFORMATION;

D O I：

10.1109/ijcnn48605.2020.9207471

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Pattern recognition methods for classification are leveraged in the field of computational anatomy and neuroimaging showing high reliability and applicability. Body-brain human functions related to the motor-strength features can be discovered by data integration and analysis of 3D brain images, phenotype and behavioural information. This work is focused on the study of feature-based interplay of 3D brain structures with motor-strength information. In particular, this research introduces an ensemble of supervised machine learning approaches for a binary motor-strength classification (strong vs weak) based on 3D brain anatomical features. The proposed approach has been evaluated on 1113 case studies by obtaining well-defined features and reaching the average accuracy of 72% on the test set.

引用

页数：8

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