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Deep-Learning-Based System for Assisting People with Alzheimer's Disease
2022
Electronics
Preserved Fulltext
It also allows for the remote supervision and management of the nutrition program by a caregiver. ...
This research shows that, even using standard computational hardware, neural networks' training provided good predictive capabilities for the models (image classification 96%, object detection 74%, and ...
Related Works Alzheimer's disease has been subject to various studies that employ supervised learning techniques such as deep neural network (DNN), convolutional neural network (CNN), and recurrent neural ...
doi:10.3390/electronics11193229
fatcat:dn2gc26wufhzpdp2q22jjfvdyu
2020 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 42
2021
IEEE Transactions on Pattern Analysis and Machine Intelligence
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., 2195-2211 Samani, A., see Panetta, K., 2540-2551 Schwartz, G., and Nishino, K., Recognizing Material Properties from Images; 1981-1995 Sebe, N., see Pilzer, A., 2380-2395 Seddik, M., see Tamaazousti ...
Eft-
ekhari, A., +, TPAMI Nov. 2020 2901-2911
Diseases
Hierarchical Fully Convolutional Network for Joint Atrophy Localization
and Alzheimer's Disease Diagnosis Using Structural MRI. ...
., +, TPAMI Feb. 2020 318-327 Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI. ...
doi:10.1109/tpami.2020.3036557
fatcat:3j6s2l53x5eqxnlsptsgbjeebe
Classification of Alzheimer's Disease Based on Weakly Supervised Learning and Attention Mechanism
2022
Brain Sciences
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Alzheimer's datasets are small, making it difficult to train large-scale neural networks. In this paper, we propose a network model (WS-AMN) that fuses weak supervision and an attention mechanism. ...
The weakly supervised data augmentation network is used as the basic model, the attention map generated by weakly supervised learning is used to guide the data augmentation, and an attention module with ...
Our method provides a new idea for AD classification. ...
doi:10.3390/brainsci12121601
pmid:36552061
pmcid:PMC9775321
fatcat:fpms4a6senejfngqwssfufsuji
Enhancing Alzheimer's Disease Classification using 3D Convolutional Neural Network and Multilayer Perceptron Model with Attention Network
2023
KSII Transactions on Internet and Information Systems
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Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. ...
In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. ...
Acknowledgment This paper is supported by the National Natural Science Foundation of China, major instrument project number: 62027827; Name: Development of a multimodal auxiliary diagnostic equipment for ...
doi:10.3837/tiis.2023.11.002
fatcat:66iusekij5c43btycjdohlwmhq
Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers
[article]
2024
arXiv
pre-print
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Alzheimer's disease (AD), characterized by progressive cognitive decline and memory loss, presents a formidable global health challenge, underscoring the critical importance of early and precise diagnosis ...
By synergistically combining convolutional neural networks (CNNs) and vision transformers (ViTs), the 3D HCCT adeptly captures both local features and long-range relationships within 3D MRI scans. ...
The Alzheimer's Disease Neuroimaging Initiative (ADNI) deserves our deepest appreciation for providing the neuroimaging data that formed the foundation of our experiments. ...
arXiv:2403.16175v1
fatcat:ihjtj5cac5furpo532tasmolpu
2021 Index IEEE Journal of Biomedical and Health Informatics Vol. 25
2021
IEEE journal of biomedical and health informatics
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, JBHI Jan. 2021 69-76 Lacsogram: A New EEG Tool to Diagnose Alzheimer's Disease. ...
., +, JBHI July 2021 2409-2420 Lacsogram: A New EEG Tool to Diagnose Alzheimer's Disease. ...
doi:10.1109/jbhi.2022.3140980
fatcat:ufig7b54gfftnj3mocspoqbzq4
A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises
[article]
2020
arXiv
pre-print
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It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. ...
We conclude with a discussion and presentation of promising future directions. ...
Weakly or partially supervised learning. In [54] , Wang et al. solve a weakly-supervised multi-label disease classification from a chest x-ray. ...
arXiv:2008.09104v1
fatcat:z2gic7or4vgnnfcf4joimjha7i
A Review of Deep Learning Algorithms and Their Applications in Healthcare
2022
Algorithms
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Deep learning uses artificial neural networks to recognize patterns and learn from them to make decisions. ...
Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain. ...
Acknowledgments: The authors would like to thank the Arab Open University for supporting this research paper.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/a15020071
fatcat:ku5mfuijdjfxxdv7hlkexad7dy
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
2024
Bioengineering
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, thus presenting a pioneering solution for clinical practice. ...
In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment ...
Ensemble methods: You can enhance the accuracy of a model by combining predictions from multiple weakly supervised models or by incorporating different sources of weak supervision. ...
doi:10.3390/bioengineering11030219
pmid:38534493
pmcid:PMC10967767
fatcat:tdrqch5tinhhbmnk7bkf4ilsv4
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
2021
Sensors
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As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be ...
We also outline the limitations of existing techniques and discuss potential directions for future research. ...
directions such as weakly supervised learning and semi-supervised learning. ...
doi:10.3390/s21144758
pmid:34300498
pmcid:PMC8309939
fatcat:jytyt4u2pjgvhnhcto3vcvd3a4
Attention-effective multiple instance learning on weakly stem cell colony segmentation
[article]
2022
arXiv
pre-print
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As a single model, we employ a U-net-like convolution neural network (CNN) to train on binary image-level labels for MIL colonies classification. ...
To maximize the efficiency in categorizing colony conditions, we propose a multiple instance learning (MIL) in weakly supervised settings. ...
CONCLUSIONS This study proposed a single network multiple instance learning in a weakly supervised settings based on U-net like architecture for annotating colonies and classifying the colony conditions ...
arXiv:2203.04606v1
fatcat:fwvyqg4uwvcbrpxpra5dygayxm
Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology
[article]
2023
arXiv
pre-print
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., principal and independent component analyses) or deep representation learning (e.g., auto-encoders, self-supervision). ...
Identifying abnormal patterns in electroencephalography (EEG) remains the cornerstone of diagnosing several neurological diseases. ...
We also evaluated three classifiers, Gaussian Naive Bayes, a support vector machine, and a shallow neural network, for each feature. The results are shown in Table II . ...
arXiv:2211.13793v2
fatcat:7w3hoxtpjnazhbqzqfnjn56f2e
Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks
2022
Frontiers in Computational Neuroscience
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non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild ...
In recent years, predicting and modeling the progression of Alzheimer's disease (AD) based on neuropsychological tests has become increasingly appealing in AD research.Objective: In this study, we aimed ...
Weakly supervised deep learning for brain disease prognosis using MRI and incomplete clinical scores. IEEE Trans. ...
doi:10.3389/fncom.2021.769982
pmid:35069161
pmcid:PMC8770936
fatcat:lurmwqdvfrd2lmwiy7gxfmnrcq
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
[article]
2019
arXiv
pre-print
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Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. ...
This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. ...
We depict a deep neural network in Fig. 1 for illustration. ...
arXiv:1902.05655v1
fatcat:mjplenjrprgavmy5ssniji4cam
Transfer Learning for Oral Cancer Detection using Microscopic Images
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2011.11610v1 fatcat:ccqzrilqlzemfmrnhflqygh6zu
In this work, we present the first results of neural networks for oral cancer detection using microscopic images. ...
Overall, we obtain a 10-15% absolute improvement with transfer learning methods compared to a simple Convolutional Neural Network baseline. ...
Chestx-ray8:
Hospital-scale chest x-ray database and
benchmarks on weakly-supervised classi-
fication and localization of common tho-
rax diseases. ...
arXiv:2011.11610v2
fatcat:o4mbp6si4fdotgo7rdex2hpkfi
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