Robust algorithm for early detection of Alzheimer's disease using multiple feature extractions

Mathew, Jesia; Mekkayil, Lasitha; Ramasangu, Hariharan; Karthikeyan, B.; Manjunath, Aditya G.

doi:10.1109/indicon.2016.7839026

Cited by 12 publications

(2 citation statements)

References 12 publications

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“…After preprocessing, data were passed to the DL model called LeNet, which achieved an accuracy of 96.85%. Similarly, Mathew et al 14 considered a subset of ADNI database (151 MRI images from patients, including 71 NC and 87 AD) and applied several preprocessing methods, such as image cropping, resize, normalization, and reorientation. Principal component analysis (PCA) and discrete wavelet transform (DWT) are used to extract features followed by classification using support vector machine (SVM) and achieved an accuracy of 91% and 84% for MCI versus CN, and AD versus CN, respectively.…”

Section: Related Workmentioning

confidence: 99%

Mutliresolutional ensemble PartialNet for Alzheimer detection using magnetic resonance imaging data

Razzak

Naz²,

Ashraf³

et al. 2022

Int J of Intelligent Sys

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Alzheimer's disease (AD) is an irreversible and progressive disorder where a large number of brain cells and their connections degenerate and die, eventually destroy the memory and other important mental functions that affect memory, thinking, language, judgment, and behavior. Not a single test can effectively determine AD; however, CT and magnetic resonance imaging (MRI) can be used to observe the decrease in size of different areas (mainly temporal and parietal lobes). This paper proposes an integrative deep ensemble learning framework to obtain better predictive performance for AD diagnosis. Unlike DenseNet, we present a multiresolutional ensemble PartialNet tailored to Alzheimer detection using brain MRIs. PartialNet incorporates the properties of identity mappings, diversified depth as well as deep supervision, thus, considers feature reuse that in turn results in better learning. Additionally, the proposed ensemble PartialNet demonstrates better characteristics in terms of vanishing gradient, diminishing forward flow with better training time, and a low number of parameters compared with DenseNet. Experiments

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Section: Related Workmentioning

confidence: 99%

Mutliresolutional ensemble PartialNet for Alzheimer detection using magnetic resonance imaging data

Razzak

Naz²,

Ashraf³

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…It is possible to find the shape, size and position of abnormalities using MRI. Detecting Alzheimer's disease by doctors requires diligent medical analysis along with other physical and neurological exams [1]. It is a strenuous job for the technicians to categorize and analyze these images manually.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the performance metrics of different machine learning classifiers by combined feature extraction method in Alzheimer's disease detection

A.J¹

2019

IJETER

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The rapid growth of machine learning technology has a great advantage in developing high performance neural network algorithms which can extract low-to-high-level features in human MRI images. Classification of clinical data for Alzheimer's disease has always been challenging as currently there is no clinical test for Alzheimer's disease. Doctors diagnose it by conducting assessments of patients' cognitive decline. But it's particularly difficult for them to identify mild cognitive impairment at an early stage when symptoms are less obvious. Also, it is difficult to predict whether patients will develop Alzheimer's disease or not. The accurate diagnosis of Alzheimer's disease in the early stage is important to take preventive measures and to reduce the severity and progression before irreversible brain damages occur. The effectiveness of abnormality detection depends on the accuracy and robustness of the algorithm used. Different machine learning techniques with different levels of sensitivity, specificity, and accuracy have been developed. In this paper, performance measures of different classification algorithms like Support Vector Machine, k-Nearest-Neighbor, Discriminant Analysis Model Pseudo Linear, Discriminant Analysis Model Pseudo Quadra and Binary Decision Tree are compared and analyzed to obtain the most accurate classification method for the early prediction of Alzheimer's disease.

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Feature Extraction Methods in Alzheimer’s Disease: A Comparative Assessment

Arora,

Sharma,

Kumar

2024

Lecture Notes in Networks and Systems

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Robust algorithm for early detection of Alzheimer's disease using multiple feature extractions

Cited by 12 publications

References 12 publications

Mutliresolutional ensemble PartialNet for Alzheimer detection using magnetic resonance imaging data

Mutliresolutional ensemble PartialNet for Alzheimer detection using magnetic resonance imaging data

Evaluating the performance metrics of different machine learning classifiers by combined feature extraction method in Alzheimer's disease detection

Feature Extraction Methods in Alzheimer’s Disease: A Comparative Assessment

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