Alzheimer’s disease (AD) is the most common chronic disease in the elderly, with a high incidence rate.[1] Accurate early-stage Alzheimer’s disease identification is essential for successful treatment and recovery. Owing to which a significant research challenge is the precise diagnosis of Alzheimer’s disease. Different researchers used various techniques to detect Alzheimer’s disease effectively however; these methods still have a lack of prediction accuracy. Deep learning has had significant success and gained popularity in the field of medical imaging in recent years. It has emerged as the method of choice for examining medical images and has drawn considerable interest in the identification of AD. When it comes to detecting AD, the deep model is more precise and effective than ordinary machine learning technologies. In this review paper, we compared various research methodologies focusing on early diagnosis of AD based on convolutional neural networks (ConvNets) with the usage of Positron Emission Tomography (PET) and magnetic resonance imaging (MRI). In comparison to networks trained with single-modal images, the network trained with multi-modal images performs better. The performance of the suggested strategy has been examined using the data set from the Alzheimer’s Disease Neuroimaging Initiative.
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14 November 2023
INTERNATIONAL CONFERENCE ON SCIENCE, ENGINEERING, AND TECHNOLOGY 2022: Conference Proceedings
18–19 November 2022
Rajkot, India
Research Article|
November 14 2023
Streamlining clinical detection of Alzheimer’s disease using a multi-modal deep learning model
Heena Panjwani;
Heena Panjwani
a)
1
Gujarat Technological University, Computer Engineering
, Ahmedabad, Gujarat, India
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Mehul Parikh
Mehul Parikh
b)
2
Lalbhai Dalpatbhai College of Engineering, Information Technology
, Ahmedabad, Gujarat, India
b)Corresponding author: [email protected]
Search for other works by this author on:
b)Corresponding author: [email protected]
AIP Conf. Proc. 2963, 020029 (2023)
Citation
Heena Panjwani, Mehul Parikh; Streamlining clinical detection of Alzheimer’s disease using a multi-modal deep learning model. AIP Conf. Proc. 14 November 2023; 2963 (1): 020029. https://doi.org/10.1063/5.0182903
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