Multi-class Classification of Alzheimer's Disease Using Deep Learning and Transfer Learning on 3D MRI Images

Alzheimer's disease (AD) poses a significant challenge for neurologists due to its progressive nature and debilitating impact on cognitive function. Recent advancements in neuroimage analysis have paved the way for innovative machine learning techniques, offering potential for substantial improvements in AD detection, diagnosis, and progression prediction. In this study, we embarked on developing a novel deep learning framework to address this critical need. Traditional manual classification methods for AD are often timeconsuming, labor-intensive, and prone to inconsistencies. Given that the brain is the primary organ affected by AD, leveraging a classification system based on brain scans presents a promising avenue for achieving more accurate and reliable results. To effectively capture the spatial information embedded within 3D MRI scans, we extended convolutional techniques to three dimensions. Classification was accomplished by strategically combining features extracted from various layers of the 3D convolutional network, with differential weights assigned to the contributions of each layer. Recognizing the potential of transfer learning to accelerate training time and enhance AD detection efficacy, we incorporated this approach into our methodology. Our proposed framework integrated transfer learning with fine-tuning, harnessing brain MRI images from three distinct classes: Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC). We explored a range of pre -trained deep learning models, including ResNet50V2 and InceptionResNetV2, for AD classification. ResNet50V2 emerged as the frontrunner, demonstrating superior classification accuracy compared to its counterparts. It achieved a remarkable training accuracy of 92.15%, followed by a sustained high testing accuracy of 91.25%. These results convincingly underscore the remarkable capabilities of deep learning methods, particularly transfer learning with ResNet50V2, in accurately detecting Alzheimer's disease using 3D MRI brain scans.