Diagnosis of Schizophrenia Disorder in MR Brain Images Using Multi-objective BPSO Based Feature Selection with Fuzzy SVM

Latha, M. Madhavi; Kavitha, Ganesan

doi:10.1007/s40846-017-0355-9

Cited by 19 publications

(7 citation statements)

References 36 publications

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“…Based on these results, it is observed that various classification studies conducted with GLCM features obtained from the left brain have been more successful compared to the literature studies, shown in Table 5. Even though there are studies in the literature that utilize GLCM features [36,37], the data sets used, the number of data points, classifiers, and the analyzed brain regions show variation in these studies. The conducted study analyzed in detail the performance of various classifiers and the textural characteristics of the regions we focused on in the right and left hemispheres for the diagnosis of schizophrenia.…”

Section: Discussionmentioning

confidence: 99%

Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques

Gengeç Benli,

Andaç

2023

Diagnostics

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Accurately diagnosing schizophrenia, a complex psychiatric disorder, is crucial for effectively managing the treatment process and methods. Various types of magnetic resonance (MR) images have the potential to serve as biomarkers for schizophrenia. The aim of this study is to numerically analyze differences in the textural characteristics that may occur in the bilateral amygdala, caudate, pallidum, putamen, and thalamus regions of the brain between individuals with schizophrenia and healthy controls via structural MR images. Towards this aim, Gray Level Co-occurence Matrix (GLCM) features obtained from five regions of the right, left, and bilateral brain were classified using machine learning methods. In addition, it was analyzed in which hemisphere these features were more distinctive and which method among Adaboost, Gradient Boost, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Naive Bayes had higher classification success. When the results were examined, it was demonstrated that the GLCM features of these five regions in the left hemisphere could be classified as having higher performance in schizophrenia compared to healthy individuals. Using the LDA algorithm, classification success was achieved with a 100% AUC, 94.4% accuracy, 92.31% sensitivity, 100% specificity, and an F1 score of 91.9% in healthy and schizophrenic individuals. Thus, it has been revealed that the textural characteristics of the five predetermined regions, instead of the whole brain, are an important indicator in identifying schizophrenia.

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Section: Discussionmentioning

confidence: 99%

Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques

Gengeç Benli,

Andaç

2023

Diagnostics

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“…The five-grade comments in equation (4) are selected to determine the concrete dam’s health state. Since the difference between comments is unknown, the paired comparison judgment matrix between comments is constructed by qualitative terms composed in a linear way, 23–25 which is illustrated in Figure 2. In Figure 2, W, S, O, H, and EH are utilized to distinguish the interval 1–9, and a1,a2,a3,a4 correspond to four truncation points.…”

Section: Methodsmentioning

confidence: 99%

A comprehensive evaluation method for concrete dam health state combined with gray-analytic hierarchy-optimization theory

Yang

et al. 2021

Structural Health Monitoring

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Concrete dam health state has been taken seriously in the field of structural health monitoring. A limitation in the current research is a lack of clear physical significance and reasonable interval division for better reflecting the development trend of the health state. This article introduces a comprehensive evaluation method that provides information on the fusion of different monitoring types and influences of degree weight. The analysis method of variation tendency for concrete dam soundness is proposed based on gray theory. According to the principle of analytic hierarchy process, this article introduces an optimization division method of evaluation grade interval for concrete dam health state. Based on this, a method based on multiple monitoring quantities is established through studies of determination methods for monitoring various quantities of weight, which aimed at evaluating concrete dam health state and solving the problem of comprehensive evaluation for concrete dam health in an effective manner. An existing concrete dam is presented and discussed to serve as the validation of the established theory.

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“…Three types of B-MFO are developed using S-shaped, V-shaped and U-shaped transfer functions to convert MFO from continuous to binary. SZ is a common brain disease, and Manohar and Ganesan studied the relationship between the image textures of SZ and normal images (Manohar and Ganesan 2018 ). With mutual information entropy as objective function, a fuzzy SVM classifier based on BPSO distinguishes SZ individuals from healthy people.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

“…( 2019 ) Binary MOA EEG Low Medium High Baig et al. ( 2017 ) DE EEG Medium Medium High Anter and Ali ( 2020 ) CSA Medical Diagnosis Fast High High Manohar and Ganesan ( 2018 ) BPSO SZ Diagnosis Medium Medium Low Chen et al. ( 2019 ) Many-objective binary PSO Healthcare Classification Low Low High Jiménez et al.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

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Diagnosis of Schizophrenia Disorder in MR Brain Images Using Multi-objective BPSO Based Feature Selection with Fuzzy SVM

Cited by 19 publications

References 36 publications

Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques

Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques

A comprehensive evaluation method for concrete dam health state combined with gray-analytic hierarchy-optimization theory

A survey on binary metaheuristic algorithms and their engineering applications

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