Optimized SVM using AdaBoost and PSO to Classify Brain Images of MR

Elahifasaee, Farzaneh

doi:10.1109/mvip53647.2022.9738549

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…This model offers increased accuracy and potential alternative to conventional clinical diagnosis methods. In [23] introduced a new method aimed at enhancing feature selection process by particle swarm optimization and weighted SVM from brain MRIs. This method achieves a promising classification accuracy of 93%, marking its effectiveness in handling large datasets and classifying brain images, demonstrating its potential for Alzheimer's disease diagnosis.…”

Section: Related Workmentioning

confidence: 99%

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SVM-Based Classifier For Early Detection Of Alzheimer's Disease

Niranjan Kumar

2024

EATP

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Alzheimer's disease (AD) is a disorder affecting the brain and its functions that results in permanent damage to the brain. This multifaceted disease slowly destroys brain cells, reducing a person's ability to think, remember, and carry out even the most basic duties. Ultimately, this cognitive decline leads to dementia. A significant portion of the global population faces metabolic challenges like Alzheimer's disease and diabetes. Recent research has explored various machine learning approaches aimed at early disease detection. Early AD diagnosis is very important for a speedy recovery and minimizing damage to brain cells. In this proposed work, a machine learning model is developed using Support Vector Machine to detect individuals with dementia (AD) or without dementia (NC). Our model is trained using 2D Magnetic Resonance Imaging (MRI) brain scan images. We computed common performance measures like the F1-score and accuracy to assess the model. The 15-fold cross-validation technique was applied to crossvalidate these results. Notably, our results demonstrate that Support Vector Machine (SVM) attained a remarkable accuracy of 99.06% in the detection of AD. Accurate Alzheimer's disease detection through machine learning algorithms can greatly reduce the annual mortality rates related to AD.

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Section: Related Workmentioning

confidence: 99%

“…The performance of SVM can be further improved with kernel function [23]. It allows to create higherdimensional, non-linear models.…”

Section: Svm -Kernel Trickmentioning

confidence: 99%

SVM-Based Classifier For Early Detection Of Alzheimer's Disease

Niranjan Kumar

2024

EATP

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Machine Learning Model and Cuckoo Search in a modular system to identify Alzheimer’s disease from MRI scan images

Thangavel

Selvaraj

2023

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Optimized Hybrid Deep Learning Framework for Early Detection of Alzheimer’s Disease Using Adaptive Weight Selection

Gasmi,

Alyami,

Hamid

et al. 2024

Diagnostics

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Background: Alzheimer’s disease (AD) is a progressive neurological disorder that significantly affects middle-aged and elderly adults, leading to cognitive deterioration and hindering daily activities. Notwithstanding progress, conventional diagnostic techniques continue to be susceptible to inaccuracies and inefficiencies. Timely and precise diagnosis is essential for early intervention. Methods: We present an enhanced hybrid deep learning framework that amalgamates the EfficientNetV2B3 with Inception-ResNetV2 models. The models were integrated using an adaptive weight selection process informed by the Cuckoo Search optimization algorithm. The procedure commences with the pre-processing of neuroimaging data to guarantee quality and uniformity. Features are subsequently retrieved from the neuroimaging data by utilizing the EfficientNetV2B3 and Inception-ResNetV2 models. The Cuckoo Search algorithm allocates weights to various models dynamically, contingent upon their efficacy in particular diagnostic tasks. The framework achieves balanced usage of the distinct characteristics of both models through the iterative optimization of the weight configuration. This method improves classification accuracy, especially for early-stage Alzheimer’s disease. A thorough assessment was conducted on extensive neuroimaging datasets to verify the framework’s efficacy. Results: The framework attained a Scott’s Pi agreement score of 0.9907, indicating exceptional diagnostic accuracy and dependability, especially in identifying the early stages of Alzheimer’s disease. The results show its superiority over current state-of-the-art techniques.Conclusions: The results indicate the substantial potential of the proposed framework as a reliable and scalable instrument for the identification of Alzheimer’s disease. This method effectively mitigates the shortcomings of conventional diagnostic techniques and current deep learning algorithms by utilizing the complementing capabilities of EfficientNetV2B3 and Inception-ResNetV2 by using an optimized weight selection mechanism. The adaptive characteristics of the Cuckoo Search optimization facilitate its application across many diagnostic circumstances, hence extending its utility to a wider array of neuroimaging datasets. The capacity to accurately identify early-stage Alzheimer’s disease is essential for facilitating prompt therapies, which are crucial for decelerating disease development and enhancing patient outcomes.

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Optimized SVM using AdaBoost and PSO to Classify Brain Images of MR

Cited by 3 publications

References 14 publications

SVM-Based Classifier For Early Detection Of Alzheimer's Disease

SVM-Based Classifier For Early Detection Of Alzheimer's Disease

Machine Learning Model and Cuckoo Search in a modular system to identify Alzheimer’s disease from MRI scan images

Optimized Hybrid Deep Learning Framework for Early Detection of Alzheimer’s Disease Using Adaptive Weight Selection

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