Performance analysis of particle swarm optimization technique in classification of dementia using MRI images

Bharanidharan, N.; Rajaguru, Harikumar

doi:10.1109/cesys.2017.8321288

Cited by 7 publications

(7 citation statements)

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“…In [85], to detect AD and MCI from ADNI MRI scans using a multiple instance learning (MIL) method is proposed based on leave-one-out cross-validation. OASIS MRI dementia data is used to classify three dementia classes: non-dementia, very mild dementia and mild dementia using particle swarm optimization (PSO) providing automated clinical decisions to diagnose dementia with an accuracy 77.4%, [78].…”

Section: A Conventional Machine Learning Approaches For Dementia Diamentioning

confidence: 99%

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Ahmed

Zhang

Feng

et al. 2019

IEEE Rev. Biomed. Eng.

101

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Dementia, a chronic and progressive cognitive declination of brain function due to disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving the accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. Much of the success of these approaches can be attributed to the application of machine learning and deep learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the previous five years (2013-2018). Based on the rigorous review of the existing works, we have found that while most of the studies focused on Alzheimer's disease have demonstrated reasonable performance, the identification of other types of dementia remains a major challenge. Hybrid imaging analysis through deep learning approaches may hold promises for early diagnosis of these other types of dementia, and warrant further investigation in the future. The main contributions of this review paper are as follows: 1. Based on the detailed analysis of the existing literature, this paper discusses the most recent neuroimaging procedures for dementia diagnosis, and 2. It systematically explains the machine learning techniques and, in particular, deep learning approaches for early detection of dementia.

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Section: A Conventional Machine Learning Approaches For Dementia Diamentioning

confidence: 99%

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Ahmed

Zhang

Feng

et al. 2019

IEEE Rev. Biomed. Eng.

101

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“…[11][12][13][14][15] If the fitness function is represented as f, then the pbest of a particle will be updated using the following equation during the iteration t: Usually, each bird updates its position and velocity depending on its personal best position (lbest) and the entire swarm best position (gbest).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…The membership value and cluster center are found by using Equations (26) and (27) while the objective function is calculated using Equation (15). The membership value and cluster center are found by using Equations (26) and (27) while the objective function is calculated using Equation (15).…”

Section: Implementation Of Fcm-based Dementia Classificationmentioning

confidence: 99%

“…Each particle i will be represented by three vectors namely current position vector (x-vector: x i ), personal best vector (p-vector: p i ), and current velocity vector (v-vector: v i ). [11][12][13][14][15] If the fitness function is represented as f, then the pbest of a particle will be updated using the following equation during the iteration t:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…The feature vector x k will be initialized as 80 statistical feature values and Euclidean distance is used to compute D ik (x k ,v i ). The membership value and cluster center are found by using Equations (26) and (27) while the objective function is calculated using Equation (15). The threshold for stopping criterion ϵ is chosen as 0.01 and the optimal value of fuzzifier (m) and a number of clusters (c) will be calculated using trial and error method as shown in Figure 19.…”

Section: Implementation Of Fcm-based Dementia Classificationmentioning

confidence: 99%

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Performance enhancement of swarm intelligence techniques in dementia classification using dragonfly‐based hybrid algorithms

Bharanidharan¹,

Rajaguru²

2019

Int J Imaging Syst Tech

Self Cite

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Most often clinicians require automated computer‐aided MRI classification techniques to substantiate the status of dementia accurately. In this research paper, dragonfly‐based features are used to improve the accuracy of well‐known swarm intelligence algorithms specifically particle swarm optimization, artificial bee colony, and ant colony optimization in dementia classification. Cross‐sectional MRI of 65 non‐dementia and 52 dementia subjects were collected from the OASIS database and analyzed. The dementia classification performance of above‐mentioned three individual swarm intelligence algorithms is compared with non‐swarm intelligence algorithm—Fuzzy C means. A further comparison was made on the improvisation of above‐mentioned swarm intelligence algorithms while using dragonfly‐based features and Fuzzy C means‐based features. Although many swarm intelligence algorithms are reported in the literature, it is ingenious to use dragonfly‐based features for improving the performance of individual swarm intelligence algorithms in dementia classification. With proper weight parameters, Dragonfly‐particle swarm optimization hybrid classifier yields the highest accuracy of 87.18%, whereas all the above‐mentioned individual classifiers yield accuracy of less than 66%.

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Improved chicken swarm optimization to classify dementia MRI images using a novel controlled randomness optimization algorithm

Bharanidharan

Rajaguru

2020

Int J Imaging Syst Tech

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The objective of this research paper is to categorize the magnetic resonance imaging (MRI) images as demented (DEM) or nondemented (ND) using improved chicken swarm optimization technique (ICSO). In literature, CSO technique is widely used to solve numerical optimization and feature selection problem. Using this optimization technique for medical image classification problem will be a pioneering idea. If this technique is directly used to classify the medical images, it provides poor results. Hence, appropriate enhancements are made on the original algorithm using a novel controlled randomness optimization algorithm and control parameter tuning. Cross‐over and Rooster selection methods are also implemented in cascaded manner for further performance improvization. All the experiments are made for two cases: with and without statistical features. The brain MRI images of 65 ND and 52 DEM subjects obtained from the Open Access Series of Imaging Studies website are used in this analysis. The ICSO without statistical features provides the highest accuracy of 86.32%, whereas the original chicken swarm optimization technique provides the accuracy of 52.13% and 52.99% with and without statistical features, respectively.

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Performance analysis of particle swarm optimization technique in classification of dementia using MRI images

Cited by 7 publications

References 1 publication

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Performance enhancement of swarm intelligence techniques in dementia classification using dragonfly‐based hybrid algorithms

Improved chicken swarm optimization to classify dementia MRI images using a novel controlled randomness optimization algorithm

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