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

Diagnostics, Journal Year: 2024, Volume and Issue: 14(3), P. 345 - 345

Published: Feb. 5, 2024

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

Language: Английский

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A Comprehensive Analysis of Deep Learning-Based Approaches for Prediction and Prognosis of Infectious Diseases

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4477 - 4497

Published: June 8, 2023

Language: Английский

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Advancing early diagnosis of Alzheimer’s disease with next-generation deep learning methods

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 96, P. 106614 - 106614

Published: July 6, 2024

Language: Английский

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A Comprehensive Analysis of Artificial Intelligence Techniques for the Prediction and Prognosis of Lifestyle Diseases

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(8), P. 4733 - 4756

Published: June 24, 2023

Language: Английский

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An Analysis of Deep Transfer Learning-Based Approaches for Prediction and Prognosis of Multiple Respiratory Diseases Using Pulmonary Images

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(2), P. 1023 - 1049

Published: Oct. 31, 2023

Language: Английский

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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

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Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT)

Pattern Analysis and Applications, Journal Year: 2024, Volume and Issue: 27(3)

Published: July 1, 2024

Abstract Cognitive disorders affect various cognitive functions that can have a substantial impact on individual’s daily life. Alzheimer’s disease (AD) is one of such well-known disorders. Early detection and treatment diseases using artificial intelligence help contain them. However, the complex spatial relationships long-range dependencies found in medical imaging data present challenges achieving objective. Moreover, for few years, application transformers has emerged as promising area research. A reason be transformer’s impressive capabilities tackling dependency two ways, i.e., (1) their self-attention mechanism to generate comprehensive features, (2) capture patterns by incorporating global context dependencies. In this work, Bi-Vision Transformer (BiViT) architecture proposed classifying different stages AD, multiple types from 2-dimensional MRI data. More specifically, transformer composed novel modules, namely Mutual Latent Fusion (MLF) Parallel Coupled Encoding Strategy (PCES), effective feature learning. Two datasets been used evaluate performance BiViT-based architecture. The first dataset several classes mild or moderate demented AD. other samples patients with AD mild, early, impairments. For comparison, transfer learning algorithm deep autoencoder each trained both datasets. results show model achieves an accuracy 96.38% dataset. when applied data, slightly decreases below 96% which resulted due smaller amount imbalance distribution. Nevertheless, given results, it hypothesized perform better if imbalanced distribution limited availability problems addressed. Graphical abstract

Language: Английский

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An Analysis of Detection and Diagnosis of Different Classes of Skin Diseases Using Artificial Intelligence-Based Learning Approaches with Hyper Parameters

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(2), P. 1051 - 1078

Published: Oct. 19, 2023

Language: Английский

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Artificial Intelligence-Based Approaches for Detection and Classification of Different Classes of Malaria Parasites Using Microscopic Images: A Systematic Review

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(8), P. 4781 - 4800

Published: June 23, 2023

Language: Английский

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Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection

Frontiers in Computational Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: April 25, 2024

Alzheimer's disease (AD) is a neurodegenerative illness that impairs cognition, function, and behavior by causing irreversible damage to multiple brain areas, including the hippocampus. The suffering of patients their family members will be lessened with an early diagnosis AD. automatic technique widely required due shortage medical experts eases burden staff. artificial intelligence (AI)-based computerized method can help achieve better accuracy precision rates. This study proposes new automated framework for AD stage prediction based on ResNet-Self architecture Fuzzy Entropy-controlled Path-Finding Algorithm (FEcPFA). A data augmentation has been utilized resolve dataset imbalance issue. In next step, we proposed deep-learning model self-attention module. ResNet-50 modified connected block important information extraction. hyperparameters were optimized using Bayesian optimization (BO) then train model, which was subsequently employed feature extracted features FEcPFA. best selected FEcPFA passed machine learning classifiers final classification. experimental process publicly available MRI achieved improved 99.9%. results compared state-of-the-art (SOTA) techniques, demonstrating improvement in terms time efficiency.

Language: Английский

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