Alzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier

Shankar, K.; Lakshmanaprabu, S.K.; Khanna, Ashish; Tanwar, Sudeep; Rodrigues, Joel J. P. C.; Roy, Nihar Ranjan

doi:10.1016/j.compeleceng.2019.06.001

Cited by 123 publications

(11 citation statements)

References 12 publications

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“…Metaheuristics approach such as spider monkey optimization algorithm, Cuckoo Search optimization, Bat Inspired Algorithm, Ant Lion Optimization, and Moth Flame Optimization has been hybridized with ML, and utilized in CoVID-19, lung cancer, Retinal artery vein, Chronic Kidney, and diabetes respectively (Kaur et al 2023 ). Metaheuristics with ML have also been utilized in the diagnosis of AD in MRI images (Shankar et al 2019 ; Chitradevi et al 2021 ; Sayed et al 2017 ). However, it is important to note that the utilization of metaheuristics in combination with ML techniques has been limited in the context of multimodal neuroimaging.…”

Section: Resultsmentioning

confidence: 99%

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Odusami,

Maskeliūnas,

Damaševičius

et al. 2023

Cogn Neurodyn

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In recent years, Alzheimer’s disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87–87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Odusami,

Maskeliūnas,

Damaševičius

et al. 2023

Cogn Neurodyn

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“…The proposed HWOA based on deep learning [ 23 ] helps to enhance the speed of the training procedure using optimally selecting the parameters pixel resolution. In the whale optimization technique, the humpback whales hunt the prey by applying three processes namely search, encircle and forming a bubble net for hunting the prey.…”

Section: Methodsmentioning

confidence: 99%

Accurate detection of brain tumor using optimized feature selection based on deep learning techniques

Ramtekkar

Pandey

Pawar

2023

Multimed Tools Appl

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An unusual increase of nerves inside the brain, which disturbs the actual working of the brain, is called a brain tumor. It has led to the death of lots of lives. To save people from this disease timely detection and the right cure is the need of time. Finding of tumor-affected cells in the human brain is a cumbersome and time- consuming task. However, the accuracy and time required to detect brain tumors is a big challenge in the arena of image processing. This research paper proposes a novel, accurate and optimized system to detect brain tumors. The system follows the activities like, preprocessing, segmentation, feature extraction, optimization and detection. For preprocessing system uses a compound filter, which is a composition of Gaussian, mean and median filters. Threshold and histogram techniques are applied for image segmentation. Grey level co-occurrence matrix (GLCM) is used for feature extraction. The optimized convolution neural network (CNN) technique is applied here that uses whale optimization and grey wolf optimization for best feature selection. Detection of brain tumors is achieved through CNN classifier. This system compares its performance with another modern technique of optimization by using accuracy, precision and recall parameters and claims the supremacy of this work. This system is implemented in the Python programming language. The brain tumor detection accuracy of this optimized system has been measured at 98.9%.

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“…The proposed model gave an improved classification accuracy, but the Prodromal stages of AD were not considered. Authors in [32] also proposed using CNN fMRI data for early AD classification. Authors in [33] also presented CNN architecture to diagnose AD early using fMRI with an accuracy of 96.7%, but the power of the method to diagnose the disease severity is low.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network

et al. 2021

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One of the first signs of Alzheimer’s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity.

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Alzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier

Cited by 123 publications

References 12 publications

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

Accurate detection of brain tumor using optimized feature selection based on deep learning techniques

Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network

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