This study presents an innovative deep learning framework for improved early detection of a debilitating neurodegenerative condition marked by cognitive decline and memory impairment. Timely diagnosis is crucial for effective interventions and improved patient outcomes. Our framework integrates diverse data sources, including structural and functional neuroimaging (MRI and PET) alongside clinical information, to enhance detection precision. Convolutional Neural Networks (CNNs) analyze structural MRI scans, extracting subtle changes in brain structure indicative of early disease progression. Functional insights are gleaned from PET scans, contributing to increased sensitivity. Additionally, longitudinal data is incorporated through Recurrent Neural Networks (RNNs) to capture the disease's temporal evolution. Training on a diverse dataset utilizes transfer learning, optimizing performance even with limited labeled data. Rigorous validation consistently demonstrates the model's effectiveness, achieving a 92% accuracy rate.
Keywords
Machine learning, Deep learning, Convolution, Accuracy, Neurodiagnosis.
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Rajkumar Govindarajan
Rajkumar Govindarajan
Department of Computer Science and Engineering, St. Peters Institute of Higher Education and Research, Avadi, Chennai, India.
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Cite this article
Rajkumar Govindarajan, Thirunadana Sikamani K, Angati Kalyan Kumar and Komal Kumar N, “The Future of Neurodiagnosis: Deep Learning for Earlier Intervention”, Journal of Machine and Computing, pp. 813-819, July 2024. doi: 10.53759/7669/jmc202404075.
