Deep Learning Architectures for Alzheimer's Disease Classification Using Convolutional Neural Networks

Alzheimer's disease (AD) continues to present a substantial challenge in modern healthcare, placing considerable burdens on individuals, families, and healthcare systems worldwide. The study investigates the efficacy of Convolutional Neural Network (CNN) architectures in the categorization of distinct phases of Alzheimer's disease using brain imaging data. The CNN model proposed in this study encompasses a multi-layered architecture, commencing with image preprocessing. Subsequently, convolutional, and pooling layers are employed, along with dropout regularization, to enhance the model's performance. Finally, fully connected layers are utilized for the purpose of classification. The study assesses the efficacy of different optimization algorithms, namely SGD, Adadelta, Adagrad, and Adam, over a span of 100 epochs. The training and validation graphs provide visual representations of the model's convergence and generalization abilities. It is observed that the Adam optimizer exhibits superior performance in terms of accuracy and loss. Furthermore, confusion matrices are utilized to illustrate the efficacy of different optimizers in accurately classifying individuals into the stages of "Mild Demented," "Very Mild Demented," "Non-Demented," and "Moderate Demented." The results indicate that the Adam optimizer achieves the highest accuracy and exhibits a lower number of misclassifications. The findings highlight the effectiveness of convolutional neural network (CNN) models, particularly when enhanced with the Adam optimization algorithm, in accurately categorizing various phases of Alzheimer's disease using brain imaging data. These results present encouraging prospects for clinical diagnosis and research in this field.