A convolutional neural networks approach in MRI image analysis for Alzhei

Artificial Intelligence (AI) and its advancements, particularly in Computer Vision, have narrowed the gap between humans and machines. The Deep Learning techniques, such as Convolutional Neural Networks (CNNs), have revolutionized image analysis by assigning importance to different aspects of an image and enabling accurate differentiation. This paper focuses on applying CNNs to detect structural changes associated with Alzheimer's disease using Magnetic Resonance Imaging (MRI). Currently, the diagnosis of Alzheimer's disease relies on a combination of clinical assessments and neurological tests. This study aims to develop and evaluate various CNN models, including VGG16, VGG19, ResNet50, ResNet101, MobileNet, MobileNetV2, InceptionV3, Xception, DenseNet121, and DenseNet169, to analyze MRI scans for Alzheimer's disease detection. The above models were trained and tested using a dataset comprising MRI scans from healthy individuals and Alzheimer's patients. By comparing the accuracy of the CNN models in detecting Alzheimer's disease from MRI scans, the study demonstrates the potential of CNNs in improving the accuracy and efficiency of Alzheimer's disease diagnosis. The findings suggest that CNN-based analysis of Alzheimer's MRI images holds promise for early detection and treatment of the disease. This research can growing body of knowledge in computer-aided medical diagnostics and underscores the significance of leveraging AI techniques to enhance healthcare outcomes.