Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Abstract

<jats:title>Abstract</jats:title><jats:p>Alzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.</jats:p>

Unsuspected Involvement of Spinal Cord in Alzheimer Disease

“…This information is very important for our understanding of how a neurodegenerative disease like AD has implications beyond the known brain atrophy: this could also have a significant impact on future modeling of brain networks. Furthermore, in case of a positive outcome, it is important to quantify the role of spinal cord features in distinguishing between AD and HC to drive the design of future studies; for this we implemented a machine learning approach for features selection, that is increasingly applied to improve diagnostic accuracy by quantitative imaging (Dauwan et al, 2016;Mirzaei et al, 2016). Finally, we quantified the contribution of spinal cord atrophy to explain the variance of clinical scores for determining its clinical relevance.…”

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

“…Brain–computer interface (BCI) systems provide a communication and control path between a computer and a human with applications in diverse areas such as medical (Burns et al, ; Yin et al, ; Sereshkeh et al, ), robotics (Lu et al, ; Chen et al, ; Sabri et al, ), the military, and games. Development of BCI systems requires a multidisciplinary approach crossing many fields including neurobiology, psychology, engineering, mathematics, and computer science that is highly affected by developments in each of those fields (Mirzaei et al, ; Mirzaei and Adeli, ; Jiao et al, ). Classification of signals related to the mental simulation of actions, referred to as motor imagery (MI; Xu et al ; Zhang et al ; Liu et al, ), is a key step in BCI applications, for example, controlling devices by imagining their motions (Porcaro et al, ).…”

A Survey on Multi-Scale Medical images Fusion Techniques: Brain Diseases

“…But during the past 30 years, only researches have been developing in its risk factors, symptoms, causes, and treatments. Nowadays throughout the world, more than 35 million people have been affected by Alzheimer's disease with its various stages [52][53][54][55][56][57].…”