Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function

Hajamohideen, Faizal; Shaffi, Noushath; Mahmud, Murni; Subramanian, Karthikeyan; Sariri, Arwa Al; Vimbi, Viswan; Abdesselam, Abdelhamid

doi:10.1186/s40708-023-00184-w

Cited by 47 publications

(9 citation statements)

References 62 publications

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“…In this approach, the brain image is divided into 90 regions of interest (ROI) out of which only informative voxels are selected and stored into a vector for the study corresponding to the baseline. These voxels were provided as input to CNN and variants of CNN such as ensemble system of deep convolutional neural networks and Siamese convolutional neural network [ 43 , 44 ] for further deep learning. The robustness of the system is then tested against the subset from ADNI.…”

Section: Methodsmentioning

confidence: 99%

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

et al. 2023

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Alzheimer’s disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer’s disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer’s disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.

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Section: Methodsmentioning

confidence: 99%

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

et al. 2023

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“…In order to have the importance ratings fall inside a particular range, such as [0, 1], the values are normalized. Finally, the network maps the importance scores to the image dimensions to create the saliency map, emphasizing the salient areas [38].…”

Section: Saliency Mapmentioning

confidence: 99%

An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning

Mahmud,

Barua,

Habiba

et al. 2024

Diagnostics

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have shown promising results in medical image analysis, including AD diagnosis from neuroimaging data. However, the lack of interpretability in deep learning models hinders their adoption in clinical settings, where explainability is essential for gaining trust and acceptance from healthcare professionals. In this study, we propose an explainable AI (XAI)-based approach for the diagnosis of Alzheimer’s disease, leveraging the power of deep transfer learning and ensemble modeling. The proposed framework aims to enhance the interpretability of deep learning models by incorporating XAI techniques, allowing clinicians to understand the decision-making process and providing valuable insights into disease diagnosis. By leveraging popular pre-trained convolutional neural networks (CNNs) such as VGG16, VGG19, DenseNet169, and DenseNet201, we conducted extensive experiments to evaluate their individual performances on a comprehensive dataset. The proposed ensembles, Ensemble-1 (VGG16 and VGG19) and Ensemble-2 (DenseNet169 and DenseNet201), demonstrated superior accuracy, precision, recall, and F1 scores compared to individual models, reaching up to 95%. In order to enhance interpretability and transparency in Alzheimer’s diagnosis, we introduced a novel model achieving an impressive accuracy of 96%. This model incorporates explainable AI techniques, including saliency maps and grad-CAM (gradient-weighted class activation mapping). The integration of these techniques not only contributes to the model’s exceptional accuracy but also provides clinicians and researchers with visual insights into the neural regions influencing the diagnosis. Our findings showcase the potential of combining deep transfer learning with explainable AI in the realm of Alzheimer’s disease diagnosis, paving the way for more interpretable and clinically relevant AI models in healthcare.

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“…At each hub of a decision tree, the calculation selects the leading part among a subset of highlights based on measurements like Gini debasement or data gain [9]. The method proceeds recursively until a halting model is met, such as reaching a most extreme tree profundity or least number of tests per leaf hub.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Application of Convolutional Neural Networks for Early Detection and Classification of Alzheimer's disease from MRI Images

Suman Kumar Swarnkar

2024

jes

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This study investigates the application of convolutional neural networks (CNNs) and traditional machine learning algorithms for the early detection and classification of Alzheimer's disease (AD) using brain Magnetic Resonance Imaging (MRI) data. We compare the performance of CNNs with Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM) on a dataset comprising MRI images from AD patients and healthy controls. Results show that CNNs achieved the highest accuracy (90.2%) and area under the receiver operating characteristic curve (AUC-ROC) of 0.95, outperforming SVM, RF, and GBM. The CNN model also exhibited high sensitivity (87.5%) and specificity (92.6%) in distinguishing between AD patients and healthy controls. These findings highlight the effectiveness of CNN-based approaches in leveraging raw MRI images for accurate and early detection of AD.

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Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function

Cited by 47 publications

References 62 publications

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning

Application of Convolutional Neural Networks for Early Detection and Classification of Alzheimer's disease from MRI Images

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