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This research introduces an innovative approach that combines machine learning and graph theory to aid in the diagnosis and monitoring of Alzheimer’s disease (AD)Alzheimer’s Disease (AD). Alzheimer’s, a neurodegenerative disorder affecting cognitive function, requires early diagnosis for effective intervention. Resting-state fMRIFunctional Magnetic Resonance Imaging (FMRI) is crucial in identifying brain connectivity changes associated with AD. Graph theory quantifies brain network integration and segregation, shedding light on the disease’s pathology. By merging fMRI data and graph theory, this approach allows researchers to analyze brain connectivity at a macroscopic level, improving diagnostic accuracy. The study has two main objectives: distinguishing cognitively normal individuals from those with AD using machine learning and predicting disease progression in those with Mild Cognitive Impairment (MCI). This work showcases the potential of integrating machine learning and graph theory to enhance AD diagnosis and monitoring, offering the promise of earlier interventions and improved quality of life for AD patients. It contributes to the intersection of medical imaging, machine learning, and neuroscience research, advancing strategies against neurodegenerative disorder. The convolutional neural network (CNN) stands out as a superior model for Alzheimer’s diseaseAlzheimer’s Disease (AD) prediction due to its adeptness in analyzing complex image data from brain scans. CNN excels in identifying intricate patterns and subtle abnormalities crucial in diagnosing Alzheimer’s. Leveraging its deep learning architecture, CNN efficiently processes and extracts intricate features from neuroimaging data, enabling accurate classification and early detection of Alzheimer’s. Its capacity to recognize minute structural changes in the brain through imaging techniques such as MRI and PET scans makes it a leading model in predicting and diagnosing this neurodegenerative condition, offering promising prospects in advancing early intervention and treatment.

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Detection of Alzheimers Using Imaging and Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Somu Geetha Sravya,
  • Kamsala Tharun,
  • B. R. Sandhya,
  • Tresa Biju,
  • Gopi Sagar

摘要

This research introduces an innovative approach that combines machine learning and graph theory to aid in the diagnosis and monitoring of Alzheimer’s disease (AD)Alzheimer’s Disease (AD). Alzheimer’s, a neurodegenerative disorder affecting cognitive function, requires early diagnosis for effective intervention. Resting-state fMRIFunctional Magnetic Resonance Imaging (FMRI) is crucial in identifying brain connectivity changes associated with AD. Graph theory quantifies brain network integration and segregation, shedding light on the disease’s pathology. By merging fMRI data and graph theory, this approach allows researchers to analyze brain connectivity at a macroscopic level, improving diagnostic accuracy. The study has two main objectives: distinguishing cognitively normal individuals from those with AD using machine learning and predicting disease progression in those with Mild Cognitive Impairment (MCI). This work showcases the potential of integrating machine learning and graph theory to enhance AD diagnosis and monitoring, offering the promise of earlier interventions and improved quality of life for AD patients. It contributes to the intersection of medical imaging, machine learning, and neuroscience research, advancing strategies against neurodegenerative disorder. The convolutional neural network (CNN) stands out as a superior model for Alzheimer’s diseaseAlzheimer’s Disease (AD) prediction due to its adeptness in analyzing complex image data from brain scans. CNN excels in identifying intricate patterns and subtle abnormalities crucial in diagnosing Alzheimer’s. Leveraging its deep learning architecture, CNN efficiently processes and extracts intricate features from neuroimaging data, enabling accurate classification and early detection of Alzheimer’s. Its capacity to recognize minute structural changes in the brain through imaging techniques such as MRI and PET scans makes it a leading model in predicting and diagnosing this neurodegenerative condition, offering promising prospects in advancing early intervention and treatment.