NeuroMindscape: Deep Learning Unraveling Alzheimer's Stages Through MRI-Based Classification

A degenerative condition, Alzheimer's disease can result in a gradual deterioration of the capacity to carry out basic tasks, often beginning with mild memory loss. Alzheimer's disease is the predominant contributor to the onset of dementia, affecting approximately 1 in 9 adults aged 65 and older (10.7%) [14]. Lately, there has been a lot of promise in employing deep learning, especially the Convolutional Neural Networks (CNN), in various medical imaging research works, including Alzheimer's Disease (AD). This research focuses on improving Convolutional Neural Network (CNN) model for accurate classification of 4 stages of dementia - mild, moderate, non-mild and very mild. This research introduces a model comprising seven layers, showcasing less complexity compared to its predecessors. Despite its simplicity, it demonstrates remarkable proficiency in multi-class classification tasks, achieving high-performance levels. With this model, the publicly available Kaggle Alzheimer's Disease (AD) dataset [16] was accessed, which contains 6400 MRI images with the total size of 36 Megabytes, and another Kaggle dataset [17] which adopted GAN (Generative adversarial network) based approach on the former dataset to produce synthetic images to address the imbalance issue. Using the original Kaggle Alzheimer's dataset and through methods of traditional augmentation to balance out of the classes, The proposed model successfully distinguished between the four stages of the disease with 97.05% accuracy which outperformed most of its other predeceasing CNN models, and on the Kaggle GAN-based dataset [17] the model achieved 97% accuracy.