International Journal of Education & Applied Sciences Research
Print ISSN : 2349 –4808
Online ISSN : 2349 –2899
Frequency : Continuous
Current Issue : Volume 8 , Issue 1
2021
Saisuman Singamsetty
Saisuman Singamsetty, Senior Consultant San Antonio, TX-78259, USA
DOI : https://doi.org/10.5281/zenodo.14889013 Page No : 23-32
Published Online : 2021-06-30
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This investigation presents a groundbreaking deep learning approach for the early recognition and differentiation of Alzheimer's Disease (AD), a widespread neurological disorder, employing non-invasive cerebral imaging techniques. By combining state-of-the-art deep learning algorithms with feature fusion methodologies, the research seeks to boost the accuracy and consistency of AD diagnosis, potentially facilitating earlier intervention and individualized treatment approaches. The framework utilizes multiple imaging modalities, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), to extract complementary features that reflect both structural and functional brain alterations associated with AD. An advanced feature fusion strategy is implemented to integrate these heterogeneous data sources into a unified representation, allowing the deep learning model to discern more intricate and distinctive patterns of neurodegeneration. The deep neural network, trained on an extensive, clinically diverse dataset, is engineered to identify subtle AD biomarkers, even in preclinical phases, offering a robust tool for healthcare professionals. By concentrating on non-invasive brain imaging techniques, the proposed methodology presents a promising alternative to traditional diagnostic procedures, which often involve invasive or time-consuming examinations. The integration of clinical expertise and sophisticated machine learning in this framework provides a powerful resource for neurological specialists, enhancing diagnostic precision and supporting more informed therapeutic decisions. Ultimately, this study showcases the potential of deep learning and feature fusion techniques to revolutionize the clinical approach to AD diagnosis, marking a significant advancement toward more efficient and accessible healthcare solutions. |
Keywords: Alzheimer's Disease, Deep Learning, Feature Fusion, Non-Invasive Imaging, MRI, PET, Neurodegeneration Detection, Early Diagnosis
