Convolutional Neural Network based Framework for Early Detection of Alzheimer’s Disease
Keywords:
Alzheimer’s Disease (AD), AD Neuroimaging Initiative (ADNI) Convolutional Neural Networks (CNNs), Deep Learning (DL), ImageAbstract
Alzheimer’s Disease (AD) is a degenerative brain disorder that leads to the slow decline of memory function over time, with no definitive cure currently available. Early detection is crucial for ensuring patients receive appropriate care. Numerous studies have utilized statistical and deep learning techniques to diagnose AD. Recently, deep learning techniques especially Convolutional Neural Networks (CNNs) have become increasingly popular for the early detection of Alzheimer’s disease. However, many existing CNN models, often employed through transfer learning, exhibit high computational complexity and large memory footprints. This project proposes an efficient CNN model for early AD detection, aiming to reduce computational complexity and memory footprint. The model is designed to predict AD across four categories: mild dementia, very mild dementia, non-dementia, and moderate dementia. The proposed model’s performance is compared against established pre-trained models to assess its efficiency in terms of classification accuracy and memory size. Specifically, our model achieves an accuracy of 98.77%, surpassing the accuracies of existing pre-trained models, including InceptionV3, VGG16, and ResNet50, by approximately 57.81%, 48.89%, and 48.74%, respectively. Also, the proposed model significantly reduced the memory requirements to 8.09MB when compared to InceptionV3 (83.17MB), VGG16 (56.13MB), and ResNet50 (89.98MB). This comparison shows the effectiveness of our proposed model in improving accuracy for early AD detection while addressing concerns regarding classification accuracy and memory usage.
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