Handwritten Based Alzheimer Disease Prediction from One Dimensional Datasets Using Deep Learning
DOI:
https://doi.org/10.32628/IJSRST24113112Keywords:
Alzheimer Disease, Deep Learning Techniques, Diagnostic Model, Handwritten Images, Machine LearningAbstract
Because to their high cost, sensitivity, and difficulty in completing surgeries, brain-related disorders are among the most challenging conditions. On the other hand, since the procedure's outcomes could be negative, the operation itself does not have to succeed. Alzheimer's disease, which affects adults and causes varied degrees of memory loss and knowledge forgetfulness, is one of the most prevalent brain diseases. based on the state of each patient. For these reasons, using user-handwritten datasets to categorise memory loss and determine the patient's evaluation of Alzheimer's disease at every given level is crucial. This work offers a novel method for predicting Alzheimer's disease by using advanced deep learning techniques on handwritten data. Alzheimer's is a degenerative. Alzheimer's disease, a neurological condition that progresses and necessitates prompt diagnosis and appropriate treatment. Traditional diagnostic techniques are mostly based on clinical evaluations and imaging, which are frequently inaccessible and expensive. This study investigates the unrealized potential of handwritten data as a special kind of Alzheimer's disease prediction. The dataset provides a broad picture of cognitive impairments by containing handwritten samples collected from individuals in varying states of cognition. Using deep learning architectures, such as the multi-layer perceptron method, the suggested model takes use of the temporal dependencies found in sequential handwritten patterns. These designs show promise for ensuring the temporal characteristics of handwritten data are captured with subtle features. The handwritten input is converted into a format suitable for deep learning using feature extraction techniques, which helps with efficient model training. A thorough assessment of the model's performance is conducted using common metrics including specificity, sensitivity, and accuracy. The goal is to determine whether the model can correctly forecast Alzheimer's disease based just on the unique features present in handwritten samples.
Downloads
References
Faisal, Fazal Ur Rehman, and Goo-Rak Kwon. "Automated detection of Alzheimer’s disease and mild cognitive impairment using whole brain MRI." IEEE Access 10 (2022): 65055-65066. DOI: https://doi.org/10.1109/ACCESS.2022.3180073
Basher, Abol, et al. "Volumetric feature-based Alzheimer’s disease diagnosis from sMRI data using a convolutional neural network and a deep neural network." IEEE Access 9 (2021): 29870-29882. DOI: https://doi.org/10.1109/ACCESS.2021.3059658
Abrol, Anees, et al. "Deep residual learning for neuroimaging: an application to predict progression to Alzheimer’s disease." Journal of neuroscience methods 339 (2020): 108701. DOI: https://doi.org/10.1016/j.jneumeth.2020.108701
De Silva, S.. "Alzheimer's Disease Diagnosis using Functional and Structural Neuroimaging Modalities." Enabling Technology for Neurodevelopmental Disorders. Routledge, 2021. 162-183.
Garcia‐Gutierrez, Fernando, et al. "Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning‐aided neuropsychological assessment using feature engineering and genetic algorithms." International Journal of Geriatric Psychiatry 37.2 (2022). DOI: https://doi.org/10.1002/gps.5667
Zhang, Jie, et al. "A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification." Magnetic Resonance Imaging 78 (2021): 119-126. DOI: https://doi.org/10.1016/j.mri.2021.02.001
Liu, Manhua, et al. "A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease." Neuroimage 208 (2020): 116459. DOI: https://doi.org/10.1016/j.neuroimage.2019.116459
Mendoza-Léon, Ricardo, et al. "Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders." Computers in biology and medicine 116 (2020): 103527. DOI: https://doi.org/10.1016/j.compbiomed.2019.103527
Kumar, L. Sathish, et al. "AlexNet approach for early-stage Alzheimer’s disease detection from MRI brain images." Materials Today: Proceedings 51 (2022): 58-65. DOI: https://doi.org/10.1016/j.matpr.2021.04.415
Sreelakshmi, Nagarajan Ganapathy, and Ramakrishnan Swaminathan. "Classification of alzheimer condition using MR brain images and inception-residual network model." Current Directions in Biomedical Engineering 7.2 (2021): 763-766. DOI: https://doi.org/10.1515/cdbme-2021-2195
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
