Artificial intelligence based Alzheimer’s disease detection using deep feature extraction

Kapadnis, Manav Nitin; Bhattacharyya, Abhijit; Subaşı, Abdülhamit

doi:10.1016/b978-0-443-18450-5.00007-4

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…Using medical imaging data, Kapadnis et al [16] proposed an approach that explored the use of deep learning techniques for the detection of AD. The authors used CNNs for feature extraction and a support vector machine (SVM) for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, we implement this advanced deep learning method on a more diverse multi-class dataset than in these previous studies. In a manner similar to [16,19], we utilize a CNN model for feature selection and GCNs for classifying the different AD stages, but anticipate that our proposed (CNN-GCN) method has higher accuracy in detecting AD compared to the CNNs-only model. Additionally, we work with a multi-class imbalanced dataset, as in [17], so GCNs could be a better classification technique for predicting different dementia stages.…”

Section: Literature Reviewmentioning

confidence: 99%

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New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification

Hasan,

Wagler

2024

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Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer’s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD. Therefore, we propose a cutting-edge, computer-assisted method based on an advanced deep learning algorithm to differentiate between people with varying degrees of dementia, including healthy, very mild dementia, mild dementia, and moderate dementia classes. In this paper, four separate models were developed for classifying different dementia stages: convolutional neural networks (CNNs) built from scratch, pre-trained VGG16 with additional convolutional layers, graph convolutional networks (GCNs), and CNN-GCN models. The CNNs were implemented, and then the flattened layer output was fed to the GCN classifier, resulting in the proposed CNN-GCN architecture. A total of 6400 whole-brain medical reasoning imaging scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative database to train and evaluate the proposed methods. We applied the 5-fold cross-validation (CV) technique for all the models. We presented the results from the best fold out of the five folds in assessing the performance of the models developed in this study. Hence, for the best fold of the 5-fold CV, the above-mentioned models achieved an overall accuracy of 45.47%, 71.17%, 99.06%, and 100%, respectively. The CNN-GCN model, in particular, demonstrates excellent performance in classifying different stages of dementia. Understanding the stages of dementia can assist biotech industry researchers in uncovering molecular markers and pathways connected with each stage.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification

Hasan,

Wagler

2024

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“…Research has focused on using ML and DL technology to create algorithms that recognize, process, and extract data from neuroimaging to detect AD with a high specificity and sensitivity (Silva-Spínola et al, 2022). For instance, Kapadnis et al (2023) discusses the use of deep feature extraction methods for early AD detection. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting.…”

Section: Introductionmentioning

confidence: 99%

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Mirkin

Albensi

2023

Front. Aging Neurosci.

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Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

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Analysis of Clinical, Genetic, and Demographic Data for Prediction of Alzheimer's Disease with Machine Learning

Petreska,

Nikolovski,

Novotni

et al. 2024

2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)

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Artificial intelligence based Alzheimer’s disease detection using deep feature extraction

Cited by 3 publications

References 51 publications

New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification

New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Analysis of Clinical, Genetic, and Demographic Data for Prediction of Alzheimer's Disease with Machine Learning

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