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674 results for “brain disease”
BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 2. Attributes of the classification models used in the experiments
<p>The authors used for their experiments a data set (UCI, 2016) containing 756 records about persons with thyroid dysfunctions. The classification model has 22 attributes; the class attribute is the target and it has three possible values: hypothyroidism, hyperthyroidism and normal. The current data set was extracted and preprocessed from the original file. A description of the attributes used in the experiments is given in Figure 2 (an extract from thyroid.arff test file). </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 19. Accuracy of classification using the three methods: KNN, SVM and our method for MCI subjects
<p>Whatever the patient condition, Normal, MCI or AD, our method has provided us with better results. Advocate Example precision for Normal Patients was found 96% as opposed to 88% for the SVM method and 84% for KNN. For MCI patients was found 88% as opposed to 80% for the SVM method and 72% for KNN. Also for AD patients were found 92% as opposed to 88% for the SVM method and 80% for KNN. Our classification method gave us the best results, finding overall accuracy of 92% as opposed to 84% for the SVM method and 78.66% for KNN. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 20. The accuracy of classification using the three methods, KNN, SVM and our method, for AD subjects
<p>Whatever the patient condition, Normal, MCI or AD, our method has provided us with better results. Advocate Example precision for Normal Patients was found 96% as opposed to 88% for the SVM method and 84% for KNN. For MCI patients was found 88% as opposed to 80% for the SVM method and 72% for KNN. Also for AD patients were found 92% as opposed to 88% for the SVM method and 80% for KNN. Our classification method gave us the best results, finding overall accuracy of 92% as opposed to 84% for the SVM method and 78.66% for KNN. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 18. Accuracy of classification using the three methods, KNN, SVM and our method, for normal subjects
<p>We present three figures representing the accuracy of the classification using the three methods, KNN, SVM and our method for normal, MCI and Alzheimer subjects. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 17. The results of calculating the Hausdorff distances
<p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD for the four methods<br> (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a Normal subject following the<br> Corpus Calosum segmentation. </p> <p> </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 16. Results of calculating the Hausdorff distances
<p>Results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a subject Normal following segmentation of the Corpus Calosum.</p>
BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 3. KNIME Diagram
<p>The proposed KNIME diagram representing the data mining models is given in Figure 3. The nodes that constitute the model diagram are: ARFF Reader – the input node used to load the data set in arff format, Partitioning – the node with the role of data set partition (for training and for the validation of the classification model), Naive Bayes Learner and Decision Tree Learner – the nodes used to build the classification model, Naive Bayes Predictor and Decision Tree Predictor – the nodes used to validate the model, Scorer – the node reports a confusion matrix and the accompanying quality measures in its view, Normalizer – the data set are normalized to be able to apply the neural network models, Multilayer Perceptron and RBFNetwork – the nodes corresponding to the neural network classification models, Weka Predictor – a node implemented in Weka to validate the models. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 15. The results of calculating the Hausdorff distances
<p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD for the four methods (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a Normal subject following the Corpus Calosum segmentation.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 14. A comparison between the results
<p> A comparison between the results. Each column shows the superposition of the corresponding results: Caselle (yellow curve), Chan & Vese (curve blue) Lankton (red curve), our method (purple line) and the ground truth (Curve Green) for a normal subject, MCI and AD.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 13.The results of the Corpus Calosum segmentation.
<p> The results of the Corpus Calosum segmentation. The six lines present in order: image zoom on the hippocampus area, manually segmented image, the result of the Caselle method , the result of the Chan&Vese method, the result of the Lankton method, the result of our method. Column 1 shows a healthy subject, column 2 MCI (primary stage), and the third column corresponds to an Alzheimer's subject (advanced stage). </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 12. Results of calculating the Hausdorff distances
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 11. Results of calculating the Hausdorff distance
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p> Results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles Chan & Vese, Lanktom, our method) .and the ground truth about a MCI subject following segmentation of the hippocampus.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 10. The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles, Chan & Vese, Lanktom, and our method) and the ground truth about a subject Normal following the segmentation of the hippocampus</p> <p> </p>
BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 1. Factors that Affect Thyroid Function (The Institute for Functional Medicine, 2014)
<p> In Figure 1 are presented the main factors that affect the thyroid function. It is obvious that factors such as stress, infection, toxins, trauma and certain medication are directly responsible for the improper production of thyroid hormones. Symptoms identification and the early detection of abnormal values of thyroid hormones after clinical investigation will help in establishing the proper diagnostic and to prescribe the right medication. The patient must periodically evaluate his clinical state in order to receive the treatment as long as he needs it. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 9. A comparison between the results
<p>In figure 9 we present a comparison between the manual segmentation, Caselle, Chan&vese, Lankton and our method. </p> <p>A comparison between the results. Each column shows the superposition of the corresponding results: Caselle (yellow curve), Chan & Vese (curve blue), Lankton (red curve), our method (purple curve), and the ground truth (Green Curve) for a normal subject, MCI and AD.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 8. The results of the segmentation of the hippocampus.
<p>The results show the hippocampus segmentation using both Caselle, Chan&Vese, Lankton and our method. </p> <p>The results of the segmentation of the hippocampus. The six lines present: image zoom on the hippocampus area, manually segmented image, the result of the Caselle method, the result of the Chan&Vese method, the result of the Lankton method, the result of our method. Column 1 shows a healthy subject, column 2 shows a MCI (primary stage) and the third column corresponds to an Alzheimer's subject (advanced stage)</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 7. Training for classification
<p>AMED measures the average distance while HD measures the maximum distance between the two vectors. The aim of our method is to classify the test subject in three classes (N, MCI or AD), so for each vector element E we look for the four nearest neighbors. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 5. Improvement using variation
<p>In the figures below a constraint of the variation is used in order to show the limits. The contour may include more areas surrounding the hippocampus, which are not homogeneous with the desired area. Through the confidence interval of variation and priori knowledge can overcome these limitations. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 6. Improvement using Surface
<p>In the figures below, a constraint of the surface is used to show the limits. The contour may include the hippocampus and more areas surrounding it, which are homogeneous with the desired area. Through the surface of the confidence interval and priori knowledge can overcome these limitations. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 2. Three Corpus Calosum: Normal, MCI, AD
<p>The three figures above present the Corpus Callosum relating to three topics: Normal Topic by MCI (Mild Cognitive Impairment), Alzheimer’s topic. Secondly, we will present our clustering method to classify the test subject between 3 classes: N (Normal), MCI (Mild Cognitive Impairment), and AD (Alzheimer's disease). </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.