Alzheimer's disease (AD) is one of the most common public health issues the world is facing today. This disease has a high prevalence primarily in the elderly accompanying memory loss and cognitive decline. AD detection is a challenging task which many authors have developed numerous computerized automatic diagnosis systems utilizing neuroimaging and other clinical data. MRI scans provide high-intensity visible features, making these scans the most widely used brain imaging technique. In recent years deep learning has achieved leading success in medical image analysis. But a relatively little investigation has been done to apply deep learning techniques for the brain MRI classification. This paper explores the construction of several deep learning architectures evaluated on brain MRI images and segmented images. The idea behind segmented images investigates the influence of image segmentation step on deep learning classification. The image processing presented a pipeline consisting of pre-processing to enhance the MRI scans and post-processing consisting of a segmentation method for segmenting the brain tissues. The results show that the processed images achieved a better accuracy in the binary classification of AD vs. CN (Cognitively Normal) across four different architectures. ResNet architecture resulted in the highest prediction accuracy amongst the other architectures (90.83% for the original brain images and 93.50% for the processed images).
College of Information and Computer Sciences（信息与计算机科学学院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
In recent years, many papers have reported state-of-the-art performance on Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural networks. However, we discover that when we split that data into training and testing sets at the subject level, we are not able to obtain similar performance, bringing the validity of many of the previous studies into question. Furthermore, we point out that previous works use different subsets of the ADNI data, making comparison across similar works tricky. In this study, we …
Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin Institute of Health (BIH)（柏林健康研究所）
Attribution methods are an easy to use tool for investigating and validating machine learning models. Multiple methods have been suggested in the literature and it is not yet clear which method is most suitable for a given task. In this study, we tested the robustness of four attribution methods, namely gradient*input, guided backpropagation, layer-wise relevance propagation and occlusion, for the task of Alzheimer's disease classification. We have repeatedly trained a convolutional neural network (CNN) with identical training settings in order to separate structural MRI data of patients with …
The University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）
Mild cognitive impairment (MCI) conversion prediction, i.e., identifying MCI patients of high risks converting to Alzheimer's disease (AD), is essential for preventing or slowing the progression of AD. Although previous studies have shown that the fusion of multi-modal data can effectively improve the prediction accuracy, their applications are largely restricted by the limited availability or high cost of multi-modal data. Building an effective prediction model using only magnetic resonance imaging (MRI) remains a challenging research topic. In this work, we propose a multi-modal multi-instan…
Faculty of Science, Northern Border University（北部边境大学理学院）；SEU Saudi Electronic University（沙特电子大学）
Alzheimer's disease is a neurogenerative disease that alters memories, cognitive functions leading to death. Early diagnosis of the disease, by detection of the preliminary stage, called Mild Cognitive Impairment (MCI), remains a challenging issue. In this respect, we introduce, in this paper, a powerful classification architecture that implements the pre-trained network AlexNet to automatically extract the most prominent features from Magnetic Resonance Imaging (MRI) images in order to detect the Alzheimer's disease at the MCI stage. The proposed method is evaluated using a big database from …
Institute of Smart Systems and Artificial Intelligence, Nazarbayev University（纳扎尔巴耶夫大学智能系统与人工智能研究所）
Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. The advances in machine learning and publicly available medical datasets initiated multiple studies in AD diagnosis. In this work, we utilize a multi-modal deep learning approach in classifying normal cognition, mild cognitive impairment and AD classes on the basis of structural MRI and diffusion tensor imaging (DTI) scans from the OASIS-3 dataset. In addition to a conventional multi-modal network, we also present an input agnostic architecture that allows diagnosis with either sMRI or DTI s…
School of Information Technology（信息技术学院）；University of Cincinnati（辛辛那提大学）
Alzheimer's disease is a degenerative brain disease. Being the primary cause of Dementia in adults and progressively destroys brain memory. Though Alzheimer's disease does not have a cure currently, diagnosing it at an earlier stage will help reduce the severity of the disease. Thus, early diagnosis of Alzheimer's could help to reduce or stop the disease from progressing. In this paper, we proposed a deep convolutional neural network-based model for learning model using to determine the stage of Dementia in adults based on the Magnetic Resonance Imaging (MRI) images to detect the early onset o…
We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves $9.5\%$ better Alzheimer's disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20% of the training time compared to 3D CNN models. The code is available online: h…
JSS Academy of Higher Education and Research（JSS高等教育与研究学院）；University of Delhi（德里大学）
Early detection of Alzheimer disease is crucial for deploying interventions and slowing the disease progression. A lot of machine learning and deep learning algorithms have been explored in the past decade with the aim of building an automated detection for Alzheimer. Advancements in data augmentation techniques and advanced deep learning architectures have opened up new frontiers in this field, and research is moving at a rapid speed. Hence, the purpose of this survey is to provide an overview of recent research on deep learning models for Alzheimer disease diagnosis. In addition to categoriz…
Recent studies suggest that combined analysis of Magnetic resonance imaging~(MRI) that measures brain atrophy and positron emission tomography~(PET) that quantifies hypo-metabolism provides improved accuracy in diagnosing Alzheimer's disease. However, such techniques are limited by the availability of corresponding scans of each modality. Current work focuses on a cross-modal approach to estimate FDG-PET scans for the given MR scans using a 3D U-Net architecture. The use of the complete MR image instead of a local patch based approach helps in capturing non-local and non-linear correlations be…
Indian Institute of Technology Indore（印度理工学院印多尔分校）
Alzheimer's Disease (AD) is a neurodegenerative disease that is one of the significant causes of death in the elderly population. Many deep learning techniques have been proposed to diagnose AD using Magnetic Resonance Imaging (MRI) scans. Predicting AD using 2D slices extracted from 3D MRI scans is challenging as the inter-slice information gets lost. To this end, we propose a novel and lightweight framework termed 'Biceph-Net' for AD diagnosis using 2D MRI scans that model both the intra-slice and inter-slice information. Biceph-Net has been experimentally shown to perform similar to other S…
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis
Huacong Qingjiao Information Technology (Beijing) Co., Ltd.（华聪青教信息技术（北京）有限公司）；Chongqing Technology and Business University（重庆工商大学）；University of Maine at Presque Isle（缅因大学普雷斯克艾尔分校）；San Francisco Bay University（旧金山湾大学）；Universitario Tecnológico Universitam；Northern Arizona University（北亚利桑那大学）
University of Engineering and Technology Taxila（塔克西拉工程技术大学）；University of Central Florida（中佛罗里达大学）
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory. An early detection can prevent the patient from further damage of the brain cells and hence avoid permanent memory loss. In past few years, various automatic tools and techniques have been proposed for diagnosis of AD. Several methods focus on fast, accurate and early detection of the disease to minimize the loss to patients mental health. Although machine learning and deep learning techniques have significantly improved medical imaging systems for A…
3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation
Vo Thien Nhan Institute of Engineering, Ho Chi Minh City University of Technology (HUTECH), Vietnam（越南胡志明市技术大学（HUTECH）工程学院）；Ho Bac Nam Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh City (VNU-HCM), Vietnam（越南胡志明市技术大学（HCMUT）计算机科学与工程学院，越南胡志明市国家大学（VNU-HCM））
Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease
Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算学部）；Harbin No.3 High School（哈尔滨市第三中学）；School of Life Science and Technology, Harbin Institute of Technology（哈尔滨工业大学生命科学与技术学院）；College of Public Health, George Mason University（乔治梅森大学公共卫生学院）
There is a need for automatic diagnosis of certain diseases from medical images that could help medical practitioners for further assessment towards treating the illness. Alzheimers disease is a good example of a disease that is often misdiagnosed. Alzheimers disease (Hear after referred to as AD), is caused by atrophy of certain brain regions and by brain cell death and is the leading cause of dementia and memory loss [1]. MRI scans reveal this information but atrophied regions are different for different individuals which makes the diagnosis a bit more trickier and often gets misdiagnosed [1…
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal elders. However, it remains difficult to early predict when and which mild cognitive impairment (MCI) individuals will convert to AD dementia. Informed by pattern classification studies which have demonstrated that pattern classifiers built on longitudinal data could achieve better classification performance than those built on cross-sectional data, we develop a deep learning model based on recurrent neural networks…
Increasing effort in brain image analysis has been dedicated to early diagnosis of Alzheimer's disease (AD) based on neuroimaging data. Most existing studies have been focusing on binary classification problems, e.g., distinguishing AD patients from normal control (NC) elderly or mild cognitive impairment (MCI) individuals from NC elderly. However, identifying individuals with AD and MCI, especially MCI individuals who will convert to AD (progressive MCI, pMCI), in a single setting, is needed to achieve the goal of early diagnosis of AD. In this paper, we propose a deep ordinal ranking model f…
Deep Convolutional Neural Networks (CNNs) are becoming prominent models for semi-automated diagnosis of Alzheimer's Disease (AD) using brain Magnetic Resonance Imaging (MRI). Although being highly accurate, deep CNN models lack transparency and interpretability, precluding adequate clinical reasoning and not complying with most current regulatory demands. One popular choice for explaining deep image models is occluding regions of the image to isolate their influence on the prediction. However, existing methods for occluding patches of brain scans generate images outside the distribution to whi…
Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin Institute of Health（柏林健康研究所）；Bernstein Center for Computational Neuroscience（伯恩斯坦计算神经科学中心）；Berlin Center for Advanced Neuroimaging（柏林高级神经影像中心）；Technical University Berlin（柏林工业大学）
Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems. In this study, we train a 3D CNN to detect Alzheimer's disease based on structural MRI scans of the brain. Then, we apply four different gradient-based and occlusion-based visualization methods that explain the network's classification decisions by highlighting relevant areas in the input image. We compare the methods qualitatively and quantitatively. We find that all four methods focus on brain regions known to be involved in Alzheimer's diseas…
In recent days, Convolutional Neural Networks (CNN) have demonstrated impressive performance in medical image analysis. However, there is a lack of clear understanding of why and how the Convolutional Neural Network performs so well for image analysis task. How CNN analyzes an image and discriminates among samples of different classes are usually considered as non-transparent. As a result, it becomes difficult to apply CNN based approaches in clinical procedures and automated disease diagnosis systems. In this paper, we consider this issue and work on visualizing and understanding the decision…
Effective and accurate diagnosis of Alzheimer's disease (AD) or mild cognitive impairment (MCI) can be critical for early treatment and thus has attracted more and more attention nowadays. Since first introduced, machine learning methods have been gaining increasing popularity for AD related research. Among the various identified biomarkers, magnetic resonance imaging (MRI) are widely used for the prediction of AD or MCI. However, before a machine learning algorithm can be applied, image features need to be extracted to represent the MRI images. While good representations can be pivotal to the…
McMaster University（麦克马斯特大学）；Rotman Research Institute at Baycrest, University of Toronto（多伦多大学贝克莱斯特罗特曼研究所）；Ryerson University（瑞尔森大学）
Recently, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is a powerful machine learning algorithm in classification while extracting low to high-level features. In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain. The importance of classifying this kind of medical data …
Manchester Metropolitan University（曼彻斯特城市大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal form mild cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has provided a cost-effective and objective way for early prevention and treatment of disease progression, leading to improved patient care. In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from sMRI scans. Different from the existing approaches, the novelty of our approach is thre…
Technical University of Denmark（丹麦技术大学）；University of Copenhagen（哥本哈根大学）；Rigshospitalet（丹麦国家医院）
Convolutional neural networks have enabled significant improvements in medical image-based diagnosis. It is, however, increasingly clear that these models are susceptible to performance degradation when facing spurious correlations and dataset shift, leading, e.g., to underperformance on underrepresented patient groups. In this paper, we compare two classification schemes on the ADNI MRI dataset: a simple logistic regression model using manually selected volumetric features, and a convolutional neural network trained on 3D MRI data. We assess the robustness of the trained models in the face of…
Collaborative Attention and Consistent-Guided Fusion of MRI and PET for Alzheimer's Disease Diagnosis
School of Software Yunnan University（软件学院 云南大学）；Department of Computer Science The University of Sheffield（计算机科学系 剑桥大学）；Department of Computing Xian JiaoTong-Liverpool University（计算系 西交利物浦大学）
Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification
Department of Neurology, Medical University of Graz（格拉茨医科大学神经科）；Institute of Biomedical Imaging, Graz University of Technology（格拉茨技术大学生物医学成像研究所）；BioTechMed-Graz（格拉茨BioTechMed）
Nowadays, due to tremendous improvements in high performance computing, it has become easier to train Neural Networks. We intend to take advantage of this situation and apply this technology in solving real world problems. There was a need for automatic diagnosis certain diseases from medical images that could help a doctor and radiologist for further action towards treating the illness. We chose Alzheimer disease for this purpose. Alzheimer disease is the leading cause of dementia and memory loss. Alzheimer disease, it is caused by atrophy of the certain brain regions and by brain cell death.…
