Alzheimer Detection from MRI Scans using Deep Learning Techniques

Alzheimer's disease (AD) is a neurodegenerative disease that affects millions of people worldwide, and early diagnosis is essential for effective treatment. This work focuses on using deep learning models to classify AD stages from brain MRI scans. We use three convolutional neural network (CNN) architectures (ResNet50, EfficientNetB0, and sequential CNN), which are selected due to their unique advantages in measurement accuracy, efficiency, and complexity. ResNet50 is selected for its depth and ability to capture complex features, while EfficientNetB0 provides a balance between efficiency and effectiveness, making it suitable for limited environments. Sequential CNN has a simpler structure and achieves high accuracy, making it a strong candidate for fine classification tasks. Addressing non-conformance issues and improving operating standards. Using this method, we achieved over 95% accuracy, demonstrating the effectiveness of the model in detecting AD at various stages. This study demonstrates the potential of CNNs to diagnose Alzheimer's disease, providing great solutions for early diagnosis and personalized treatment planning. Future work will focus on optimizing these models for clinical use to improve patient outcomes.