Classification of Alzheimer’s disease using MRI data based on Deep Learning Techniques

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(2), P. 101940 - 101940

Published: Jan. 24, 2024

Alzheimer's Disease (AD) is a worldwide concern impacting millions of people, with no effective treatment known to date. Unlike cancer, which has seen improvement in preventing its progression, early detection remains critical managing the burden AD. This paper suggests novel AD-DL approach for detecting AD using Deep Learning (DL) Techniques. The dataset consists pictures brain magnetic resonance imaging (MRI) used evaluate and validate suggested model. method includes stages pre-processing, DL model training, evaluation. Five models autonomous feature extraction binary classification are shown. divided into two categories: without Data Augmentation (without-Aug), CNN-without-AUG, (with-Aug), CNNs-with-Aug, CNNs-LSTM-with-Aug, CNNs-SVM-with-Aug, Transfer learning VGG16-SVM-with-Aug. main goal build best accuracy, recall, precision, F1 score, training time, testing time. recommended methodology, showing encouraging results. experimental results show that CNN-LSTM superior, an accuracy percentage 99.92%. outcomes this study lay groundwork future DL-based research identification.

Language: Английский

---

AI-Driven Innovations in Alzheimer's Disease: Integrating Early Diagnosis, Personalized Treatment, and Prognostic Modelling

Ageing Research Reviews, Journal Year: 2024, Volume and Issue: unknown, P. 102497 - 102497

Published: Sept. 1, 2024

Language: Английский

---

Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review

Neural Processing Letters, Journal Year: 2024, Volume and Issue: 56(3)

Published: April 24, 2024

Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide, making early detection essential for effective intervention. This review paper provides comprehensive analysis the use deep learning techniques, specifically convolutional neural networks (CNN) and vision transformers (ViT), classification AD using brain imaging data. While previous reviews have covered similar topics, this offers unique perspective by providing detailed comparison CNN ViT classification, highlighting strengths limitations each approach. Additionally, presents an updated thorough most recent studies in field, including latest advancements architectures, training methods, performance evaluation metrics. Furthermore, discusses ethical considerations challenges associated with models such as need interpretability potential bias. By addressing these issues, aims to provide valuable insights future research clinical applications, ultimately advancing field techniques.

Language: Английский

---

ConvADD: Exploring a Novel CNN Architecture for Alzheimer's Disease Detection

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(4)

Published: Jan. 1, 2024

Alzheimer's disease (AD) poses a significant healthcare challenge, with an escalating prevalence and forecasted surge in affected individuals. The urgency for precise diagnostic tools to enable early interventions improved patient care is evident. Despite advancements, existing detection frameworks exhibit limitations accurately identifying AD, especially its stages. Model optimisation accuracy are other issues. This paper aims address this critical research gap by introducing ConvADD, advanced Convolutional Neural Network architecture tailored AD detection. By meticulously designing study endeavours surpass the of current methodologies enhance metrics, optimisation, reliability diagnosis. dataset was collected from Kaggle consists preprocessed 2D images extracted 3D images. Through rigorous experimentation, ConvADD demonstrates remarkable performance showcasing potential as robust effective. proposed model shows results tool 98.01%, precision 98%, recall F1-Score only 2.1 million parameters. However, despite promising results, several challenges remain, such generalizability across diverse populations need further validation studies. elucidating these gaps challenges, contributes ongoing discourse on improving lays groundwork future domain.

Language: Английский

---

Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques

International Journal of Imaging Systems and Technology, Journal Year: 2023, Volume and Issue: 34(1)

Published: Sept. 14, 2023

Abstract Alzheimer's disease (AD) is a degenerative neurological disorder with incurable characteristics. To identify the substantial solution, we used structural biomarker (structural magnetic resonance imaging) to see neurostructural changes in different regions of brain AD, mild cognitive impairment, and normal subjects. In this study, detected AD their subtypes by using traditional machine learning ensemble models. It also identified relative impact score various cortical subcortical subtypes. Experimental study contains two levels classification: binary multiclass. The Ensemble_LR_SVM model classification has 99% accuracy detection. Random forest multiclass 82% accuracy. cortical‐subcortical analysis, right hemisphere's parahippocampal entorhinal were discovered be most influential. Similarly, inferior temporal isthmus cingulate had significant influence left hemisphere.

Language: Английский

---

Deep-Learning Based Multi-Modalities Fusion for the Detection of Brain-Related Diseases: A Review

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 149 - 170

Published: Jan. 1, 2025

Language: Английский

---

Enhancing multi-class neurodegenerative disease classification using deep learning and explainable local interpretable model-agnostic explanations

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: April 1, 2025

Alzheimer's disease (AD) and Parkinson's (PD) are two of the most prevalent neurodegenerative disorders, necessitating accurate diagnostic approaches for early detection effective management. This study introduces deep learning architectures, Residual-based Attention Convolutional Neural Network (RbACNN) Inverted (IRbACNN), designed to enhance medical image classification AD PD diagnosis. By integrating self-attention mechanisms, these models improve feature extraction, interpretability, address limitations traditional methods. Additionally, explainable AI (XAI) techniques incorporated provide model transparency clinical trust in automated diagnoses. Preprocessing steps such as histogram equalization batch creation applied optimize quality balance dataset. The proposed achieved an outstanding accuracy 99.92%. results demonstrate that combination with XAI, facilitate precise diagnosis, thereby contributing reducing global burden diseases.

Language: Английский

---

Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis

Frontiers in Aging Neuroscience, Journal Year: 2025, Volume and Issue: 17

Published: April 10, 2025

Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by progressive degeneration of the frontal and temporal lobes, leading to significant changes in personality, behavior, language abilities. Early accurate differential diagnosis between FTD, its subtypes, other dementias, such as Alzheimer's disease (AD), crucial for appropriate treatment planning patient care. Machine learning (ML) techniques have shown promise enhancing diagnostic accuracy identifying complex patterns clinical neuroimaging data that are not easily discernible through conventional analysis. This systematic review, following PRISMA guidelines registered PROSPERO, aimed assess strengths limitations current ML models used differentiating FTD from neurological disorders. A comprehensive literature search 2013 2024 identified 25 eligible studies involving 6,544 patients with dementia, including 2,984 3,437 AD, 103 mild cognitive impairment (MCI) 20 Parkinson's or probable Lewy bodies (PDD/DLBPD). The review found Support Vector Machines (SVMs) were most frequently technique, often applied electrophysiological data. Deep methods, particularly convolutional neural networks (CNNs), also been increasingly adopted, demonstrating high distinguishing dementias. integration multimodal data, neuroimaging, EEG signals, neuropsychological assessments, has suggested enhance accuracy. showed strong potential improving diagnosis, but challenges like small sample sizes, class imbalance, lack standardization limit generalizability. Future research should prioritize development standardized protocols, larger datasets, explainable AI facilitate ML-based tools into real-world practice. https://www.crd.york.ac.uk/PROSPERO/view/CRD42024520902.

Language: Английский

---

Optimized Hybrid Deep Learning Model for Accurate Classification of Alzheimer’s Stages

Lecture notes on data engineering and communications technologies, Journal Year: 2025, Volume and Issue: unknown, P. 138 - 150

Published: Jan. 1, 2025

Language: Английский

---

A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique

Frontiers in Artificial Intelligence, Journal Year: 2025, Volume and Issue: 8

Published: April 24, 2025

A progressive brain disease that affects memory and cognitive function is Alzheimer's (AD). To put therapies in place potentially slow the progression of AD, early diagnosis detection are essential. Early these phases enables activities, which essential for controlling disease. address issues with limited data computing resources, this work presents a novel deep-learning method based on using newly proposed hyperparameter optimization to identify hyperparameters ResNet152V2 model classifying AD more accurately. The compared state-of-the-art models divided into two categories: transfer learning classical showcase its effectiveness efficiency. This comparison four performance metrics: recall, precision, F1 score, accuracy. According experimental results, efficient effective various phases.

Language: Английский

---