Unveiling Alzheimer's Disease Diagnosis with Convolutional Neural Networks: Visualizing Features and Class Activation Mapping

This research paper explores the utilization of CNNs for diagnosing Alzheimer's disease, delving into techniques that enhance their interpretability and decision-making process. Feature visualization and class activation mapping provide insights into how CNNs detect the disease, emphasizing the importance of interpretability in medical imaging. The methodology involved skull stripping to isolate brain structures by removing non-brain tissues, followed by skull cropping to focus on the region of interest, improving the CNN model's accuracy and efficiency. Through training on the preprocessed dataset, the research achieved an impressive 99.92% accuracy in Alzheimer's disease detection, highlighting CNNs' potential for diagnosis. The study underscores the significance of understanding neural network decision-making, offering valuable insights into deep-learning models for medical imaging tasks. These findings contribute to improved Alzheimer's detection methods and enhance our understanding of applying deep-learning models in medical imaging, benefiting other diagnostic imaging tasks and advancing disease detection.