Early Detection of Alzheimer’s Disease Using Medical Imaging: A Review of Intelligent Approaches

Alzheimer’s Disease (AD) is a progressive, irreversible, and neuro-degenerative disease with a long pre-clinical period, affecting brain cells and leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very late stage, after irreversible damage to the brain cells has occurred, when there is no scope to prevent further cognitive decline. Non-invasive neuroimaging procedures capable of detecting AD at the preliminary stages are crucial for providing treatment and retarding disease progression and have proven to be a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, consisting of magnetic resonance imaging (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4%, whereas convolutional neural networks (CNN) have a mean accuracy of 78.5%. Better classification accuracy has been achieved by combining MRI and PET, rather than using a single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioral, and genetic) produced superlative results. However, promising results have been achieved, and still, there is room for performance improvement of the proposed methods, assisting healthcare professionals and clinicians.