Depression detection from sMRI and rs-fMRI images using machine learning

Mousavian, Marzieh; Chen, Jianhua; Traylor, Zachary; Greening, Steven G.

doi:10.1007/s10844-021-00653-w

Cited by 16 publications

(7 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A thorough mapping analysis involving 46 meticulously chosen research articles on the evaluation and treatment of psychiatric conditions utilizing electroencephalography and deep learning was conducted in [33]. The resemblance and disparity of spatial cubes in brain MRI images are used as parameters in a machine-learning approach for depression identification that was published in [34]. To test the efficacy of depression detection, an empirical study was performed using publicly available Facebook data, and a machine-learning approach was developed.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent-Based System for Detecting Depression on Social Media Platform

Ojo,

Adewuyi,

Oni

et al. 2023

Preprint

View full text Add to dashboard Cite

Purpose: The global quarantine measures implemented in response to the coronavirus outbreak have resulted in a surge of psychiatric issues, including but not limited to depressive symptoms, anxiety, and sleep disturbances, which have placed a significant burden on healthcare systems worldwide. The most efficacious prevention strategy for depression is the identification of its early warning symptoms. In recent times, the healthcare sector has increasingly embraced a technology-oriented strategy. The examination of social media content possesses the capability to significantly reduce the incidence of depression and, consequently, the number of suicides. Methods: The present study introduces a computational method to detect social media users who exhibit symptoms of depression. The intelligent framework employs machine learning techniques to identify text in social media posts that pertain to depression. In this study, three distinct machine learning models, namely the bidirectional encoder representations from Transformers (BERT), the long short-term memory (LSTM), and the convolutional neural network (CNN) were subjected to 1 Springer Nature 2021 L A T E X template Article Title pre-processing and pre-training using data sets obtained from the Twitter API and the Reddit database. The ultimate goal of this study is to accurately distinguish between depressive and non-depressive content. Results: According to the empirical analysis, the BERT model demonstrated a 92.4% accuracy rate and achieved the most optimal validation loss during the third epoch. Moreover, in comparison to the CNN and LSTM models, the BERT model exhibited greater efficiency across all performance indicators. Conclusion: The BERT model performed exceptionally well in both text categorization and feature extraction when compared to the other pre-training models utilized in this investigation. The BERT model has been refined by the identification of these unique characteristics, making it better suited to the task of identifying depressive-related information in digital documents and social media posts.

show abstract

Section: Related Workmentioning

confidence: 99%

An Intelligent-Based System for Detecting Depression on Social Media Platform

Ojo,

Adewuyi,

Oni

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These pre-processing tasks are essential for transforming the data such that it can be treated with ML. In this case study, we analyse a workflow which uses fMRIPrep as a sub-workflow to process MRI data before it is used to predict whether the person in question has depression [81]. As mentioned in Section 6.2.1, this fMRIPrep automated workflow is built on top of nipype to perform pre-processing of fMRI data [32,31].…”

Section: Cs Nipypementioning

confidence: 99%

“…The case study implicitly defines a workflow through its use of Python scripts 43 . However, Mousavian et al represent the workflow as explicit blocks in the article [81]. Therefore it is straightforward to classify each task in the workflow as DS or ML as done for the other case studies.…”

Section: Solution Workflow Layermentioning

confidence: 99%

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

Oakes¹,

Famelis²,

Sahraoui³

2022

Preprint

View full text Add to dashboard Cite

Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents six key challenges that a domain expert faces in transforming their problem into a computational workflow, and then into an executable implementation. These challenges arise out of our conceptual framework which presents the "route" of options that a domain expert may choose to take while developing their solution.To ground our conceptual framework in the stateof-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for these six challenges. Case studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation.The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.

show abstract

“…For example, Mao et al used support vector machines (SVM) to detect adolescent depression patients based on regional homogeneity (ReHo), which was obtained from restingstate functional magnetic resonance imaging (rs-fMRI) data [6] . Mousavian et al utilized the similarity/dissimilarity of fMRI voxels to diagnose depression by machine learning [7] . Vigneshwaran et al used regional homogeneity of voxels from MRI as feature to diagnose ASD among males [8] .…”

Section: Introductionmentioning

confidence: 99%

“…Figure 4(b) and Eq. (6) (7) shows the calculation process of distance to weights : 6 7 where is the one-dimensional Gaussian probability density function with mean of 0 and variance of 1.…”

mentioning

confidence: 99%

Multi-classifier fusion base on belief-value for the diagnosis of neuropsychiatric disorders

Zhao

Lü

et al. 2023

Preprint

View full text Add to dashboard Cite

Neuropsychiatric disorders seriously affect the health of patients, and early diagnosis and treatment are crucial to improve the quality of patients’ life. Machine learning and other related methods can be used for disease diagnosis and prediction, among which multi-classifier fusion method has been widely studied due to its significant performance over single classifiers. In this paper, we propose a multi-classifier fusion classification framework based on belief-valuefor the neuropsychiatric disorders diagnosis. Specifically, the belief-value measures the belief level of different samples by considering information from two perspectives, which are distance information (the output distance of the classifier) and local density information (the weight of the nearest neighbor samples on the test samples). The proposed belief-value is more representative compared to the belief-value which only uses a single type of information. Further, based on the concept of multi-view learning, we performed the calculation of the belief-values under the sample space with different features, and the complementary relationship between different belief-values was captured by a multilayer perceptual (MLP) network. Compared with majority voting and linear fusion methods, the MLP network can better capture the nonlinear relationship between belief-values, which produces better diagnostic results. Experimental results show that the proposed method outperforms single classifier and multi-classifier linear fusion methods for the diagnosis of neuropsychiatric disorders.

show abstract

Depression detection from sMRI and rs-fMRI images using machine learning

Cited by 16 publications

References 67 publications

An Intelligent-Based System for Detecting Depression on Social Media Platform

An Intelligent-Based System for Detecting Depression on Social Media Platform

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

Multi-classifier fusion base on belief-value for the diagnosis of neuropsychiatric disorders

Contact Info

Product

Resources

About