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Controlling the progression of Alzheimer’s disease (AD), a progressive neurological illness that results in memory loss, cognitive decline, and behavioral changes, and improving the quality of life for patients depend on early detection. Cognitive testing and neuroimaging technologies like as MRI and PET scans are used in conventional diagnostic approaches; however, these methods are often expensive, time-consuming, and require professional interpretation. Convolutional Neural Networks (CNNs), a subset of deep learning techniques, have demonstrated promise in the automated processing of medical pictures for the diagnosis of AD in recent years. This study looks at how CNNs can be used to predict Alzheimer’s disease from structural MRI scans. It provides information on how well CNN-based models perform for early diagnosis and highlights how deep learning could revolutionize AD detection.

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Alzheimer’s Disease Prediction Using CNN

  • 章节

  • 发表日期：

    2026年

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  • Vishank Agrohi,
  • Sanjeev Thakur

摘要

Controlling the progression of Alzheimer’s disease (AD), a progressive neurological illness that results in memory loss, cognitive decline, and behavioral changes, and improving the quality of life for patients depend on early detection. Cognitive testing and neuroimaging technologies like as MRI and PET scans are used in conventional diagnostic approaches; however, these methods are often expensive, time-consuming, and require professional interpretation. Convolutional Neural Networks (CNNs), a subset of deep learning techniques, have demonstrated promise in the automated processing of medical pictures for the diagnosis of AD in recent years. This study looks at how CNNs can be used to predict Alzheimer’s disease from structural MRI scans. It provides information on how well CNN-based models perform for early diagnosis and highlights how deep learning could revolutionize AD detection.