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Alzheimer’s disease (AD) is a challenging medical condition to treat since it requires accurate diagnosis as soon as possible to start treatment. Using a variety of neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalography (EEG), this study presents a multi-source deep learning architecture designed for AD classification. By collecting a variety of biomarkers and patterns of brain activity, the integration of different modalities provides a thorough knowledge of the course of AD. At the heart of this system is an intuitive Graphical User Interface (GUI) that allows researchers and clinicians to dynamically choose MRI, PET, or EEG data inputs according to specific diagnostic requirements or preferences. This GUI improves flexibility and customisation in the diagnostic workflow by streamlining the data input process.

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GUI-Based Multi-model Approach for Alzheimer’s Detection and Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Anirudh Deshpande,
  • Nandan Illur,
  • Roshith Hegde,
  • Satish Chikkamath,
  • Kaushik Mallibhat,
  • S. R. Nirmala

摘要

Alzheimer’s disease (AD) is a challenging medical condition to treat since it requires accurate diagnosis as soon as possible to start treatment. Using a variety of neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalography (EEG), this study presents a multi-source deep learning architecture designed for AD classification. By collecting a variety of biomarkers and patterns of brain activity, the integration of different modalities provides a thorough knowledge of the course of AD. At the heart of this system is an intuitive Graphical User Interface (GUI) that allows researchers and clinicians to dynamically choose MRI, PET, or EEG data inputs according to specific diagnostic requirements or preferences. This GUI improves flexibility and customisation in the diagnostic workflow by streamlining the data input process.