Scholarlink

Alzheimer’s disease (AD) is a neurological illness that affects memory and cognitive function over time. There is currently no cure or treatment to stop the disease’s progression. Early diagnosis is essential but challenging because symptoms typically don’t show up until serious brain damage has occurred. This study investigates how convolutional neural networks (CNNs), a type of deep learning, can be used to increase the precision of brain imaging diagnosis. Conv2D, MaxPooling2D, and dense layers were used to train the proposed CNN model on a Kaggle MRI dataset that was divided into three dementia levels: low, mild, and severe. SMOTE was used to resolve class imbalance, improving the model’s capacity to categorise under-represented groups. The CNN had a relatively low F1 score, a robust recall, and a high classification accuracy of 96.35%. On the same MRI dataset, comparative comparison with other neural architectures such as VGG16 and Inception V3 also showed dependable performance, underscoring CNNs’ potential to support early detection. This study highlights the potential of deep learning to transform Alzheimer’s diagnosis and improve clinical decision-making for improved disease treatment, despite difficulties in identifying small brain alterations.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Early-Stage Alzheimer’s Detection: Comparing CNN and Advanced Neural Architectures

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Krish Mithaiwala,
  • Vidhiben Ka Patel,
  • Sachin Patel,
  • Dhwanil Chauhan,
  • Margi Shah,
  • Ankur Patel

摘要

Alzheimer’s disease (AD) is a neurological illness that affects memory and cognitive function over time. There is currently no cure or treatment to stop the disease’s progression. Early diagnosis is essential but challenging because symptoms typically don’t show up until serious brain damage has occurred. This study investigates how convolutional neural networks (CNNs), a type of deep learning, can be used to increase the precision of brain imaging diagnosis. Conv2D, MaxPooling2D, and dense layers were used to train the proposed CNN model on a Kaggle MRI dataset that was divided into three dementia levels: low, mild, and severe. SMOTE was used to resolve class imbalance, improving the model’s capacity to categorise under-represented groups. The CNN had a relatively low F1 score, a robust recall, and a high classification accuracy of 96.35%. On the same MRI dataset, comparative comparison with other neural architectures such as VGG16 and Inception V3 also showed dependable performance, underscoring CNNs’ potential to support early detection. This study highlights the potential of deep learning to transform Alzheimer’s diagnosis and improve clinical decision-making for improved disease treatment, despite difficulties in identifying small brain alterations.