A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques

Hazarika, Ruhul Amin; Maji, Arnab Kumar; Sur, Samarendra Nath; Paul, Babu Sena; Kandar, Debdatta

doi:10.1109/access.2021.3072559

Cited by 24 publications

(4 citation statements)

References 175 publications

(143 reference statements)

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“…Furthermore, Random Forest (RF) was introduced to categorize the segmented cell into the normal or abnormal group at the classification level [15]. Additionally [16], there are three important classes of classification with the following: AD vs. CN, pMCI vs. CN, and pMCI vs. sMCI. Next, descriptors from PCA used in the training mode in the SVM classifier depend on extracted features with reducing samples of MRI.…”

Section: Related Workmentioning

confidence: 99%

Early Detection of Medical Image Analysis by Using Machine Learning Method

Khasawneh

Bukhari

Al‐Khasawneh

2022

Computational and Mathematical Methods in Medicine

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We develop effective medical image classification techniques, with an emphasis on histopathology and magnetic resonance imaging (MRI). The trainer utilized the curriculum as a starting point for a set of data and a restricted number of samples, and we used it as a starting point for a set of data. As calibrating a machine learning model is difficult, we used alternative methods as unsupervised feature extracts or weight-conditioning factors for identifying pathological histology pictures. As a result, the pretrained models will be trained on 3-channel RGB pictures, while the MRI sample has more slices. To alter the working model using the MRI data, the convolutional neural network (CNN) must be fine-tuned. Pretrained models are placed and then used as feature snippets. However, there is a scarcity of well-done medical photos, making training machine learning models a difficult endeavor to begin with. In any case, data augmentation aids in the generation of sufficient training samples; however, it is unclear if data augmentation aids in the prediction of unknown data samples. As a result, we fine-tuned machine learning models without using any additional data. Furthermore, rather than utilizing a standard machine learning classifier for the MRI data, we created a unique CNN that uses both 3D shear descriptors and deep features as input. This custom network identifies the MRI sample after processing our representation of the characteristics from beginning to end. On the hidden MRI dataset, our bespoke CNN outperforms traditional machine learning. Our CNN model is less prone to overfitting as a result of this. Furthermore, we have given cutting-edge outcomes employing machine learning.

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Section: Related Workmentioning

confidence: 99%

Early Detection of Medical Image Analysis by Using Machine Learning Method

Khasawneh

Bukhari

Al‐Khasawneh

2022

Computational and Mathematical Methods in Medicine

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“…CNN has been utilized for feature extraction by [12,13]. Some of the existing state-of-art works for detecting AD and classifying it was analyzed by Hazarika et al [14] depending upon the different feature extraction approaches. The dataset was re-enhanced separately with fuzzy color image enhancement, Deep Dream, and the hyper column techniques [15].…”

Section: Related Workmentioning

confidence: 99%

Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction

Kumar¹,

Prakash²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The most noteworthy neurodegenerative disorder nationwide is apparently the Alzheimer's disease (AD) which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy, a sensitive method for evaluating the AD has to be developed yet. Due to the correlations between ocular and brain tissue, the eye (retinal blood vessels) has been investigated for predicting the AD. Hence, en enhanced method named Enhanced Long Short Term Memory (E-LSTM) has been proposed in this work which aims at finding the severity of AD from ocular biomarkers. To find the level of disease severity, the new layer named precise layer was introduced in E-LSTM which will help the doctors to provide the apt treatments for the patients rapidly. To avoid the problem of overfitting, a dropout has been added to LSTM. In the existing work, boundary detection of retinal layers was found to be inaccurate during the segmentation process of Optical Coherence Tomography (OCT) image and to overcome this issue; Particle Swarm Optimization (PSO) has been utilized. To the best of our understanding, this is the first paper to use Particle Swarm Optimization. When compared with the existing works, the proposed work is found to be performing better in terms of F1 Score, Precision, Recall, training loss, and segmentation accuracy and it is found that the prediction accuracy was increased to 10% higher than the existing systems.

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“…To this end, computer-aided diagnosis systems have been built to help identify biomarkers automatically [6]. The detection of AD has greatly benefited from machine learning (ML), which is particularly helpful when working with complicated and abundant data [12,13]. The performance of classical ML algorithms may be limited by the hand-crafted feature extraction process, which makes it difficult to comprehensively mine the available data [5].…”

Section: Introductionmentioning

confidence: 99%

Machine learning for automatic Alzheimer’s disease detection: addressing domain shift issues for building robust models

Bakheet

Huang

et al. 2023

Radiology Science

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Alzheimer’s disease (AD) is a type of brain disease that affects a person’s ability to perform daily tasks. Modern neuroimaging techniques have made it possible to detect structural and functional changes in the brain that are linked to AD, and machine learning (ML)-based methods have been extensively developed to help physicians achieve fast and accurate imaging-based AD detection. One critical issue when deploying ML methods in clinical applications is the domain shift that exists between the training and test data, which may significantly attenuate a model’s performance. To resolve this issue, domain adaptation (DA) is needed to narrow the performance gap between data from domains with different distributions. The purpose of this review is to offer insight into the state of ML and DA research in the field of neuroimaging-based AD detection. The limitations of existing studies, as well as opportunities for future studies, are discussed with the hope that more investigations will be conducted in the future to optimize the clinical workflow for AD diagnosis and treatment.

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A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques

Cited by 24 publications

References 175 publications

Early Detection of Medical Image Analysis by Using Machine Learning Method

Early Detection of Medical Image Analysis by Using Machine Learning Method

Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction

Machine learning for automatic Alzheimer’s disease detection: addressing domain shift issues for building robust models

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