Machine Learning for Brain Images Classification of Two Language Speakers

Barranco-Gutiérrez, Alejandro I.

doi:10.1155/2020/9045456

Cited by 7 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One study has also used functional near-infrared spectroscopy (fNIRS) study to classify between higher and lower second language fluency groups (Lei et al, 2020). Barranco-Gutiérrez (2020) classified between adults who are native English speakers and those who learned English as a second language. Zhang et al (2023) performed both regression and classification analyses on proficiency level/comprehension scores of a second language using fMRI with a story listening task.…”

Section: Can Neuroimaging Studies Predict Literacy Skills?mentioning

confidence: 99%

From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data

Nakai,

Prado

2023

Preprint

View full text Add to dashboard Cite

The potential of using neural data to predict academic outcomes has always been at the heart of educational neuroscience, an emerging field at the crossroad of psychology, neuroscience and education sciences. Although this prospect has long been elusive, the exponential use of advanced techniques in machine learning and artificial intelligence in neuroimaging may change this state of affairs. Here we provide a review of neuroimaging studies that have used machine learning to predict literacy and numeracy outcomes in adults and children, both in the context of learning disability and typical performance. We notably review the cross-sectional and longitudinal designs used in such studies, and describe how they can be coupled with regression and classification approaches. Our review highlights the promise of these methods for predicting literacy and numeracy outcomes (and difficulties). However, we also found a large variability in terms of algorithms and underlying brain circuits across studies, and a relative lack of studies investigating longitudinal prediction of outcomes in young children before the onset of formal education. This calls for a standardization of methods in the field, as well as for a greater use of accessible and portable neuroimaging methods that have more applicability potential than lab-based neuroimaging techniques.

show abstract

Section: Can Neuroimaging Studies Predict Literacy Skills?mentioning

confidence: 99%

From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data

Nakai,

Prado

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Deep neural networks (DNNs) have good capacity to extract [70].They had a total of sixteen convolutional layers with kernel sizes of three by three, 3 FC and 5 max pooling layers. A drop-out layer has also been added to mitigate overfitting of the first two FC layers.…”

Section: Classification Of Normal Fetus Brain and Abnormalmentioning

confidence: 99%

Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

admin¹,

Abdulazeez²

2021

FPA

View full text Add to dashboard Cite

With the development of technology and smart devices in the medical field, the computer system has become an essential part of this development to learn devices in the medical field. One of the learning methods is deep learning (DL), which is a branch of machine learning (ML). The deep learning approach has been used in this field because it is one of the modern methods of obtaining accurate results through its algorithms, and among these algorithms that are used in this field are convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation and classification of ultrasound images. Finally, this study discussed the potential challenges and directions for applying deep learning in ultrasound image analysis.

show abstract

Introduction

Ghosh

2022

Data Analysis With Machine Learning for Psychologists

View full text Add to dashboard Cite

Machine Learning for Brain Images Classification of Two Language Speakers

Cited by 7 publications

References 22 publications

From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data

From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data

Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

Introduction

Contact Info

Product

Resources

About