Ensembles of Patch-Based Classifiers for Diagnosis of Alzheimer Diseases (original) (raw)
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A Comprehensive Study of Alzheimer's Disease Classification Using Convolutional Neural Networks
ArXiv, 2019
A plethora of deep learning models have been developed for the task of Alzheimer's disease classification from brain MRI scans. Many of these models report high performance, achieving three-class classification accuracy of up to 95%. However, it is common for these studies to draw performance comparisons between models that are trained on different subsets of a dataset or use varying imaging preprocessing techniques, making it difficult to objectively assess model performance. Furthermore, many of these works do not provide details such as hyperparameters, the specific MRI scans used, or their source code, making it difficult to replicate their experiments. To address these concerns, we present a comprehensive study of some of the deep learning methods and architectures on the full set of images available from ADNI. We find that, (1) classification using 3D models gives an improvement of 1% in our setup, at the cost of significantly longer training time and more computation powe...
Utilization of a convolutional method for Alzheimer disease diagnosis
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With the increasing number of cases as well as care costs, Alzheimer's disease has gained more interest in several scientific communities especially medical and computer science. Clinical and analytical tests are widely accepted techniques for detecting Alzheimer cases. However, early detection can help prevent damage to brain tissue and heal it with proper treatment. Interpreting brain images is considered as a time-consuming task with a high error-prone. Recently, advanced machine learning methods have successfully proved high performance in various fields including brain image analysis. These existing techniques, which become more used for clinical disease detection, present challenging wrongness sensibility to detect aberrant values or areas in the human brain. We conducted our work to automate the detection of the damaged areas and diagnose Alzheimer's disease. Our method can segment MRI images, identify brain lesions and the different stages of Alzheimer's disease. We evaluated our method using ample cases form public databases to demonstrate that our proposition performed reliable and effective results. Our proposal achieved an accuracy of 94.73%, a recall rate of 93.82%, and an F1-score of 92.8%. Also, the detection precision reached 91.76% with a sensitivity of 92.48% and a specificity rate of 90.64%. Our method creates an important way to optimize the imaging process via an automated computer-assisted diagnosis using potential deep learning methods to increase the consistency and accuracy of Alzheimer's disease diagnosis worldwide.
A New Deep Learning Model based on Neuroimaging for Predicting Alzheimer's Disease
The Open Bioinformatics Journal
Background: The psychological aspects of the brain in Alzheimer's disease (AD) are significantly affected. These alterations in brain anatomy take place due to a variety of reasons, including the shrinking of grey and white matter in the brain. Magnetic resonance imaging (MRI) scans can be used to measure it, and these scans offer a chance for early identification of AD utilizing classification methods, like convolutional neural network (CNN). The majority of AD-related tests are now constrained by the test measures. It is, thus, crucial to find an affordable method for image categorization using minimal information. Because of developments in machine learning and medical imaging, the field of computerized health care has evolved rapidly. Recent developments in deep learning, in particular, herald a new era of clinical decision-making that is heavily reliant on multimedia systems. Methods: In the proposed work, we have investigated various CNN-based transfer-learning strategies ...
Eswar Publications, 2024
Alzheimer's disease, an incurable neurodegenerative condition predominantly affecting memory functions in the elderly, presents a significant global health challenge, particularly among individuals aged over 65 years. Early and accurate diagnosis is crucial for effective management and intervention. However, manual diagnosis by healthcare professionals is prone to errors and time-consuming due to the increasing number of cases. While various techniques have been employed for diagnosis and classification, there remains a need for improved accuracy in early detection solutions. In this research, we propose a deep learning-based approach utilizing Convolutional Neural Network (CNN) architectures for the diagnosis and classification of Alzheimer's disease. The proposed model distinguishes Alzheimer's disease into four categories: Non-Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia. The CNN architecture, with optimized hyper parameters, demonstrated superior performance during both training and testing phases, achieving accuracy values of 0.977 and 0.994, respectively. The proposed model offers a practical solution for real-time analysis and classification of Alzheimer's disease, potentially enhancing early intervention strategies and patient care.
An Exploration: Alzheimer’s Disease Classification Based on Convolutional Neural Network
BioMed Research International, 2022
Alzheimer’s disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, ...
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI Data
2020 IEEE 23rd International Multitopic Conference (INMIC)
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory. Early detection can prevent the patient from further damage to the brain cells and hence avoid permanent memory loss. In the past few years, various automatic tools and techniques have been proposed for the diagnosis of AD. Several methods focus on fast, accurate, and early detection of the disease to minimize the loss to a patient's mental health. Although machine learning and deep learning techniques have significantly improved medical imaging systems for AD by providing diagnostic performance close to the human level. But the main problem faced during multi-class classification is the presence of highly correlated features in the brain structure. In this paper, we have proposed a smart and accurate way of diagnosing AD based on a two-dimensional deep convolutional neural network (2D-DCNN) using an imbalanced three-dimensional MRI dataset. Experimental results on Alzheimer's Disease Neuroimaging Initiative magnetic resonance imaging (MRI) dataset confirms that the proposed 2D-DCNN model is superior in terms of accuracy, efficiency, and robustness. The model classifies MRI into three categories: AD, mild cognitive impairment, and normal control; and has achieved 99.89% classification accuracy with imbalanced classes. The proposed model exhibits noticeable improvement in accuracy as compared to state-of-the-art methods.
Prediction of Alzheimer's Disease Using CNN
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer's disease diagnosis. Analysing magnetic resonance imaging (MRI) is a common practice for Alzheimer's disease diagnosis in clinical research. Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer's disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer's disease and obtains superior performance for early-stage diagnosis.
A CNN based framework for classification of Alzheimer’s disease
Neural Computing and Applications
In the current decade, advances in health care are attracting widespread interest due to their contributions to people longer surviving and fitter lives. Alzheimer's disease (AD) is the commonest neurodegenerative and dementing disease. The monetary value of caring for Alzheimer's disease patients is involved to rise dramatically. The necessity of having a computer-aided system for early and accurate AD classification becomes crucial. Deep-learning algorithms have notable advantages rather than machine learning methods. Many recent research studies that have used brain MRI scans and convolutional neural networks (CNN) achieved promising results for the diagnosis of Alzheimer's disease. Accordingly, this study proposes a CNN based end-to-end framework for AD-classification. The proposed framework achieved 99.6%, 99.8%, and 97.8% classification accuracies on Alzheimer's disease Neuroimaging Initiative (ADNI) dataset for the binary classification of AD and Cognitively Normal (CN). In multi-classification experiments, the proposed framework achieved 97.5% classification accuracy on the ADNI dataset. Keywords AD-classification Á Convolutional neural network (CNN) Á Magnetic resonance imaging (MRI) Á Adaptive momentum estimation (Adam) Á Glorot uniform weight initializer
Alzheimer's Disease Classification Using Deep CNN
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021
Especially in the world, the deep learning algorithm has become a technique of choice for analyzing medical images rapidly. Alzheimer's disease (AD) is regarded to be the most prevalent cause of dementia, and only 1 in 4 individuals with Alzheimer's are estimated to be diagnosed correctly on time. However, there is no refractory available treatment, the disorders can be managed when the loss is still mild and the treatment is most effective when it is initiated before significant downstream damage, i.e. mild cognitive impairment (MCI) or earlier steps. Physiological, neurological analysis, neurological and cognitive tests are clinically diagnosed with AD. A better diagnostic needs to be developed, which is addressed in this paper. We concentrate on Alzheimer's disease in this article and discuss different methods are available to detect Alzheimer's. Reviewed the different data sets available for studying data on Alzheimer's disease and finally comparing appropriate work done in this area.
Classification of Alzheimer's Disease Using Convolutional Neural Networks
International Research Journal of Modernization in Engineering Technology and Science
Alzheimer's disease is a progressive neurodegenerative disorder that gradually deprives the patient of cognitive function and can end in death. With the advancement of technology today, it is possible to detect Alzheimer's disease through Magnetic Resonance Imaging (MRI) scans. So that MRI is the technique most often used for the diagnosis and analysis of the progress of Alzheimer's disease. With this technology, image recognition in the early diagnosis of Alzheimer's disease can be achieved automatically using machine learning. Although machine learning has many advantages, currently the use of deep learning is more widely applied because it has stronger learning capabilities and is more suitable for solving image recognition problems. However, there are still several challenges that must be faced to implement deep learning, such as the need for large datasets, requiring large computing resources, and requiring careful parameter setting to prevent overfitting or underfitting. In responding to the challenge of classifying Alzheimer's disease using deep learning, this study propose the Convolutional Neural Network (CNN) method with the Residual Network 18 Layer (ResNet-18) architecture. To overcome the need for a large and balanced dataset, transfer learning from ImageNet is used and weighting the loss function values so that each class has the same weight. And also in this study conducted an experiment by changing the network activation function to a mish activation function to increase accuracy. From the results of the tests that have been carried out, the accuracy of the model is 88.3% using transfer learning, weighted loss and the mish activation function. This accuracy value increases from the baseline model which only gets an accuracy of 69.1%.