Alzheimer's Diagnosis with Deep Learning CNN

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, profoundly affecting individuals and healthcare systems worldwide. Early and accurate diagnosis of AD is crucial for effective intervention and management, yet traditional diagnostic methods often fall short in terms of sensitivity and precision. This research addresses the limitations of conventional diagnostic approaches by leveraging deep learning techniques, specifically convolutional neural networks (CNNs), to enhance the early detection of Alzheimer's disease. This study presents a novel deep learning-based framework designed to analyse brain imaging data for the identification of Alzheimer’s disease. The proposed system utilizes a CNN architecture to automatically learn and extract relevant features from brain scans, aiming to improve diagnostic accuracy and facilitate early detection. The framework involves several key stages: data collection and preprocessing, model development and training, performance evaluation, and system integration. The dataset used in this research comprises a diverse collection of brain imaging scans from both Alzheimer's patients and healthy controls. Advanced preprocessing techniques, including normalization and augmentation, are applied to ensure high-quality input data for model training. The CNN model is meticulously designed and tuned to capture intricate patterns associated with Alzheimer's pathology, with performance metrics such as accuracy, precision, recall, and F1-score used to evaluate its effectiveness. In summary, this research demonstrates the promise of deep learning techniques in revolutionizing Alzheimer's disease detection, paving the way for more accurate, early, and actionable diagnoses.