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Multiple ailment detection is a dangerous region of research that aim to improve healthcare outcomes by detecting multiple diseases in patients at the same time. The traditional approach to disease diagnosis entails identifying a single disease at a time, which is time-consuming, costly, and frequently results in missed diagnoses. Multiple disease detection models that can help diagnose multiple diseases accurately and quickly have been developed because of advancements in machine learning and artificial intelligence. These models identify patterns and predict the likelihood of multiple diseases using data from various sources such as medical records, lab test results, and imaging data. In this article proposing a system which used to predict dissimilar stages of Alzheimer disease which includes Non-Demented (normal), Mild-Demented, Moderate demented, and Very-Mild Demented. For prediction and classification of multiple stages of Alzheimer disease, applied Deep Learning Model known as VGG-16, using brain MRI dataset, which yields the accuracy of 98.92%, with 99% precision.

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Advancing Alzheimer’s Diagnosis: A Deep Learning-Based Multi-stage Identification Method

  • 会议论文

  • 发表日期：

    2026年

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  • K. Shilpa,
  • M. D. Gulzar,
  • L. Arokia Jesu Prahu,
  • R. Dipika Rath

摘要

Multiple ailment detection is a dangerous region of research that aim to improve healthcare outcomes by detecting multiple diseases in patients at the same time. The traditional approach to disease diagnosis entails identifying a single disease at a time, which is time-consuming, costly, and frequently results in missed diagnoses. Multiple disease detection models that can help diagnose multiple diseases accurately and quickly have been developed because of advancements in machine learning and artificial intelligence. These models identify patterns and predict the likelihood of multiple diseases using data from various sources such as medical records, lab test results, and imaging data. In this article proposing a system which used to predict dissimilar stages of Alzheimer disease which includes Non-Demented (normal), Mild-Demented, Moderate demented, and Very-Mild Demented. For prediction and classification of multiple stages of Alzheimer disease, applied Deep Learning Model known as VGG-16, using brain MRI dataset, which yields the accuracy of 98.92%, with 99% precision.