Early Detection of Alzheimer's Disease using Neuro Imaging and Deep Learning Techniques

Accurately diagnosing the stages of Alzheimer's disease (AD) using MRI images poses a challenge for medical practitioners, as even experienced professionals can make mistakes in diagnosing the stage. Traditional solutions heavily rely on various MRI pre-processing techniques to improve diagnostic accuracy. In recent years, deep learning methods, particularly Convolutional Neural Networks (CNNs), have shown promise in AD diagnosis through classification approaches. However, there is still a need to bridge the research gap in utilizing deep learning techniques for object detection in Alzheimer's disease diagnosis. In this research, comprehensive comparisons are conducted and various techniques to address this gap are explored, aiming to identify the most effective approach for solving this diagnostic challenge. We introduce hyperparameter tuning in our CNN model to identify the best parameters for achieving optimal accuracy by considering execution time. Next, we compare the accuracy and loss metrics of various CNN architectures' respective execution times. Additionally, we examine the impact of dataset augmentation techniques and the number of data samples on accuracy, loss, and execution times. Our proposed CNN model incorporates the latest techniques, featuring the EfficientNetV2S architecture. Hence, our work provides a benchmark for deep learning-based Alzheimer's disease stage detection, categorizing individuals into four stages Mild Demented, Moderate Demented, Non Demented, and Very Mild Demented. The utilization of the dataset using MRI data from the ADNI, with its extensive 2D slices for each stage, enables the extraction of meaningful features and patterns associated with Alzheimer's disease pathology. The outcomes of this study contribute to the advancement of diagnostic tools and therapeutic interventions, enhancing the understanding and management of Alzheimer's disease.