Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50 (original) (raw)
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Journal of Clinical Medicine
Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic fea...
An Optimized Machine Learning Framework for Detecting Alzheimer's Disease By MRI
JOURNAL OF ALGEBRAIC STATISTICS, 2022
Machine learning has extensive application in diverse medical fields.With advancements in medical technologies, access has been given to data for the identification of diseases in theirearly stages. Alzheimer's Disease (AD) is a chronic illnessthat will cause degeneration of the brain cells and ultimately will lead to memoryloss. AD causedcognitive mental problems like forgetfulness and confusion, as well as other symptoms such aspsychologicaland behavioralproblems, are further recommended to undergo test procedures usingneuroimagingtechniques. This work's objective is to utilize the machinelearning algorithms for processing the data acquired via neuroimaging technologies for early-stage AD detection. The framework extracts featuresusingcurvelet transform from MRI brain image. This work will also present the Decision Tree, the Adaptive Boosting (AdaBoost), and the Extreme Gradient Boosting (XGBoost) classifiers. In machine learning, Population-Based Incremental Learning (PBIL) is an optimization algorithm, in spite of being simpler than a conventional genetic algorithm, the PBIL algorithm is able to achieve much better results in several cases.PBIL is used to optimize the AdaBoost and XGBoost classifiers to improve AD classification. The experimental outcomes will demonstrate the proposed approach's superior performance over that of other existing approaches.
IEEE
Alzheimer's disease has recently become a big worry. This condition affects around 45 million people worldwide. Alzheimer's disease is a degenerative brain illness that primarily affects old adults and has no known cause or pathogenesis. Dementia is the major cause of Alzheimer's disease, which kills brain cells over time. This sickness took away people's capacity to think, read, and do many other things. By forecasting the sickness, a machine learning system can help solve this problem. The major goal is to identify Dementia in a variety of patients. This study covers the results and analyses of multiple machine learning models for diagnosing dementia. ADNI & Kaggle dataset was used. dataset is nearly 6000 MRI Images, but it contains four classes Mild demented, demented, moderate demented, very mild demented. Dataset was evaluated and used in a variety of machine learning models. For prediction, support vector machines, logistic regression, decision trees, and random forests were employed. The system was ran without fine-tuning first, and then with fine-tuning afterwards. When the results are compared, it is discovered that the support vector machine produces the best outcomes of all the models. It offers the highest sensitivity for diagnosing dementia in a large number of individuals. This system is simple and can quickly assist individuals in diagnosing Dementia..
DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images
IEEE Access, 2021
Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one's ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classifying cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconsistent due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) is utilized to create a framework that can be used to detect specific Alzheimer's disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer's disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) is proposed to detect the dementia stages from MRI. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen's Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.
A deep learning-based ensemble method for early diagnosis of Alzheimer's disease using MRI images
Research Square (Research Square), 2023
Background Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs to individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images. Method The methodology of this study was comprised of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset. The proposed method was an ensemble approach selected through a comparative analysis of various ensemble scenarios. Finally, the six best individual CNN-based classi ers were selected to combine and constitute the ensemble model. Results The evaluation showed an accuracy rate of 98.57, 96.37, 94.22, 99.83, 93.88, and 93.92, respectively, for NC/AD, NC/EMCI, EMCI/LMCI, LMCI/AD, four-way and three-way classi cation groups. The validation results on the local dataset revealed an accuracy of 88.46 for three-way classi cation. Discussion Our performance results were higher than most reviewed studies and comparable with others. Although comparative analysis showed superior results of ensemble methods against individual architectures, there were no signi cant differences among various ensemble approaches. The validation results revealed that individual models showed low performance in practice. In contrast, the ensemble method showed promising results. However, further studies on various and larger datasets are required to validate the generalizability of the model. 1. Introduction Dementia is an umbrella term for a group of neurological diseases in which cognitive capabilities deteriorate over time. Alzheimer's disease (AD), the most common type of dementia, includes 60 to 80 percent of all dementia cases (
Alzheimer's Disease Brain MRI Classification: Challenges and Insights
ArXiv, 2019
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 present the results of three splitting methods, discuss the motivations behind their validity, and report our results using all of the available subjects.
TELKOMNIKA Telecommunication Computing Electronics and Control, 2023
Alzheimer's disease (AD) is a gradually progressing neurodegenerative irreversible disorder. Mild cognitive impairment convertible (MCIc) is the clinical forerunner of AD. Precise diagnosis of MCIc is essential for effective treatments to reduce the progressing rate of the disease. The other cognitive states included in this study are mild cognitive impairment non-convertible (MCInc) and cognitively normal (CN). MCInc is a stage in which aged people suffer from memory problems, but the stage will not lead to AD. The classification between MCIc and MCInc is crucial for the early detection of AD. In this work, an algorithm is proposed which concatenates the output layers of Xception, InceptionV3, and MobileNet pre-trained models. The algorithm is tested on the baseline T1-weighted structural magnetic resonance imaging (MRI) images obtained from Alzheimer's disease neuroimaging initiative database. The proposed algorithm provided multi-class classification accuracy of 85%. Also, the proposed algorithm gave an accuracy of 85% for classifying MCIc vs MCInc, an accuracy of 94% for classifying AD vs CN, and an accuracy of 92% for classifying MCIc vs CN. The results exhibit that the proposed algorithm outruns other state-of-the-art methods for the multi-class classification and classification between MCIc and MCInc.
A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices
Computational and Mathematical Methods in Medicine
Developments in medical care have inspired wide interest in the current decade, especially to their services to individuals living prolonged and healthier lives. Alzheimer’s disease (AD) is the most chronic neurodegeneration and dementia-causing disorder. Economic expense of treating AD patients is expected to grow. The requirement of developing a computer-aided technique for early AD categorization becomes even more essential. Deep learning (DL) models offer numerous benefits against machine learning tools. Several latest experiments that exploited brain magnetic resonance imaging (MRI) scans and convolutional neural networks (CNN) for AD classification showed promising conclusions. CNN’s receptive field aids in the extraction of main recognizable features from these MRI scans. In order to increase classification accuracy, a new adaptive model based on CNN and support vector machines (SVM) is presented in the research, combining both the CNN’s capabilities in feature extraction and...
Alzheimer’s Disease (AD) is a degenerative brain disease and is the most common cause of dementia. Despite being a common disease, AD is poorly understood. Current medical treatments for AD are aimed at slowing the progression of the disease. So early detection of AD is important to intervene at an early stage of the disease. In recent years, by using machine learning predictive algorithms, assisted clinic diagnosis has received great attention due to its success of machine learning advances in the domains of computer vision. In this study, we have combined brain MRI imaging features and the features of other datatypes, and adopted various models, including XGBoost, logistic regression, and k-Nearest Neighbors, to improve AD diagnosis. We evaluated the models on the benchmark dataset of Alzheimer’s Disease Neuroimaging Initiative. Experiment results show that the logistic regression model is the top performer in terms of evaluation metrics of precision, recall, and F1-score. The pre...
An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging
Sensors
Alzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was ...