article · Journal of King Saud University - Computer and Information Sciences
This research addresses the global concern of Alzheimer's Disease (AD) by proposing a novel Deep Learning (DL) approach, termed AD-DL, for early detection. The study utilised brain Magnetic Resonance Imaging (MRI) data for evaluating and validating the suggested model. The methodology involved pre-processing, DL model training, and evaluation stages. Five distinct DL models were developed, some incorporating data augmentation, with the primary objective of achieving optimal detection accuracy, recall, precision, F1 score, training time, and testing time. Experimental results indicated that the CNN-LSTM model demonstrated superior performance, achieving an accuracy of 99.92%. These findings establish a foundation for future DL-based research in the identification of Alzheimer's Disease.
Early and accurate detection of Alzheimer's Disease is vital for patient care and management, as no effective treatment currently exists. This research offers a highly accurate, computer-aided method using brain scans, which could significantly assist healthcare professionals in identifying the condition sooner and potentially improving patient outcomes.
This early-stage research could lead to the development of advanced diagnostic tools for Alzheimer's Disease. Such tools, based on Deep Learning analysis of MRI scans, could be used by clinicians and diagnostic centres to aid in the early identification of the disease. While the results are encouraging, further research and validation would be needed to transition this into a near-market clinical application.
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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 in managing the burden of AD. This paper suggests a novel AD-DL approach for detecting early AD using Deep Learning (DL) Techniques. The dataset consists of pictures of brain magnetic resonance imaging (MRI) used to evaluate and validate the suggested model. The method includes stages for pre-processing, DL model training, and evaluation. Five DL models with autonomous feature extraction and binary classification are shown. The models are divided into two categories: without Data Augmentation (without-Aug), which includes CNN-without-AUG, and with Data Augmentation (with-Aug), which includes CNNs-with-Aug, CNNs-LSTM-with-Aug, CNNs-SVM-with-Aug, and Transfer learning using VGG16-SVM-with-Aug. The main goal is to build a model with the best detection accuracy, recall, precision, F1 score, training time, and testing time. The dataset is used to evaluate the recommended methodology, showing encouraging results. The experimental results show that CNN-LSTM is superior, with an accuracy percentage of 99.92%. The outcomes of this study lay the groundwork for future DL-based research in AD identification.
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DOI: 10.1016/j.jksuci.2024.101940
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