Multi class disorder detection of magnetic resonance brain images using composite features and neural network

Kale, Vandana V.; Hamde, Satish T.; Holambe, Raghunath S.

doi:10.1007/s13534-019-00103-1

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…Indeed, FC derived from functional magnetic resonance imaging (fMRI) approximates the statistical association of brain activity between different regions, which can be used to estimate the importance of each brain region based on the degree distributions (Bullmore & Sporns, 2009; Griffa & Van Den Heuvel, 2018; Rubinov & Sporns, 2010; Van Den Heuvel, Kahn, Goñi, & Sporns, 2012). Machine learning is a powerful tool capable of handling large‐scale datasets for constructing analytical models (Kale, Hamde, & Holambe, 2019; Park, Took, & Seong, 2018). Specifically, a regularized regression framework identifies predictor variables (such as regional FC) that correlate with response variables (such as obesity phenotypes) in a data‐driven way.…”

Section: Introductionmentioning

confidence: 99%

Whole‐brain functional connectivity correlates of obesity phenotypes

Park

Byeon

Lee

et al. 2020

Human Brain Mapping

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Dysregulated neural mechanisms in reward and somatosensory circuits result in an increased appetitive drive for and reduced inhibitory control of eating, which in turn causes obesity. Despite many studies investigating the brain mechanisms of obesity, the role of macroscale whole‐brain functional connectivity remains poorly understood. Here, we identified a neuroimaging‐based functional connectivity pattern associated with obesity phenotypes by using functional connectivity analysis combined with machine learning in a large‐scale (n ~ 2,400) dataset spanning four independent cohorts. We found that brain regions containing the reward circuit positively associated with obesity phenotypes, while brain regions for sensory processing showed negative associations. Our study introduces a novel perspective for understanding how the whole‐brain functional connectivity correlates with obesity phenotypes. Furthermore, we demonstrated the generalizability of our findings by correlating the functional connectivity pattern with obesity phenotypes in three independent datasets containing subjects of multiple ages and ethnicities. Our findings suggest that obesity phenotypes can be understood in terms of macroscale whole‐brain functional connectivity and have important implications for the obesity neuroimaging community.

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Section: Introductionmentioning

confidence: 99%

Whole‐brain functional connectivity correlates of obesity phenotypes

Park

Byeon

Lee

et al. 2020

Human Brain Mapping

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“…Second, although there are many centrality measures, we only used degree centrality to quantify complex brain networks because it is a convenient graph measure to associate brain imaging with obesity 9,11 . Different graph centrality measures quantify different aspects of the brain network, and future works should explore these 73 . Third, the number of participants was relatively small compared to that in previous studies 13 .…”

Section: Discussionmentioning

confidence: 99%

The orbitofrontal cortex functionally links obesity and white matter hyperintensities

Park

Byeon

Lee

et al. 2020

Sci Rep

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Many studies have linked dysfunction in cognitive control-related brain regions with obesity and the burden of white matter hyperintensities (WMHs). This study aimed to explore how functional connectivity differences in the brain are associated with WMH burden and degree of obesity using resting-state functional magnetic resonance imaging (fMRI) in 182 participants. Functional connectivity measures were compared among four different groups: (1) low WMH burden, non-obese; (2) low WMH burden, obese; (3) high WMH burden, non-obese; and (4) high WMH burden, obese. At a large-scale network-level, no networks showed significant interaction effects, but the frontoparietal network showed a main effect of degree of obesity. At a finer node level, the orbitofrontal cortex showed interaction effects between periventricular WMH burden and degree of obesity. Higher functional connectivity was observed when the periventricular WMH burden and degree of obesity were both high. These results indicate that the functional connectivity of the orbitofrontal cortex is affected by the mutual interaction between the periventricular WMHs and degree of obesity. Our results suggest that this region links obesity with WMHs in terms of functional connectivity.

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“…Recent findings show that ML-based diagnostics speed up the procedure and dramatically lowers the probability of making an incorrect diagnosis compared with human diagnostic methods. Preprocessing images can boost the performance of an ML-based algorithm (Kale et al, 2019). The brain MRI and CT images have poor contrast and contain various noises.…”

Section: Introductionmentioning

confidence: 99%

Building an Acute Ischemic Stroke Identification Model Using a Deep Learning Technique

Aljarallah,

Dutta,

Wahab Sait

et al. 2023

Journal of Disability Research

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Acute ischemic stroke (AIS) is a leading cause of mortality and disability. Over the past several decades, researchers proposed multiple techniques to enhance the effectiveness of AIS diagnosis. Magnetic resonance imaging (MRI) and computed tomography (CT) are widely used for assessing and treating AIS. In recent years, healthcare centers have applied the deep learning–based technique to support physicians in identifying diseases at earlier stages. Recently, researchers have employed convolutional neural network (CNN)-based image classifiers to detect diseases using complex images. However, the models demand high computation resources for generating a reasonable outcome. Thus, this study intends to build a fine-tuned CNN model for identifying AIS from MRI and CT images. The proposed framework contains three phases: image enhancement, feature extraction, and fine-tuned detection model. Initially, the researcher applies an image colorization technique using generative adversarial networks. You only look once V7 is used to extract the images’ features. In the second phase, the authors employed the Aquila optimization algorithm for tuning the hyperparameters of the Residual Network with Split attention (ResNest) model. To evaluate the performance of the ResNest model, the authors utilized two benchmark datasets of brain MRI and CT images. The MRI datasets contain 1021 healthy and 955 unhealthy images, whereas the CT datasets comprise 1551 healthy and 950 unhealthy images. The findings reveal that the ResNest model outperforms the recent approaches. The model achieves an average accuracy and F1 score of 98.25 and 97.275, and 98.65 and 98.25, for the MRI and CT datasets, respectively. In addition, the ResNest model obtained a confidence interval score of [97.84-98.13] and [97.91-98.52] for the MRI and CT datasets, respectively. The study uniquely develops a lightweight application through a compelling data preprocessing and feature extraction technique. In addition, the fine-tuned ResNest model achieves a superior outcome with limited resources. Healthcare centers can implement this lightweight model for diagnosing AIS patients.

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Multi class disorder detection of magnetic resonance brain images using composite features and neural network

Cited by 9 publications

References 23 publications

Whole‐brain functional connectivity correlates of obesity phenotypes

Whole‐brain functional connectivity correlates of obesity phenotypes

The orbitofrontal cortex functionally links obesity and white matter hyperintensities

Building an Acute Ischemic Stroke Identification Model Using a Deep Learning Technique

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