RESULT: The localization error is validated on the two datasets with superior performance over the state-of-the-art methods and variation in the expression is visualized using Principal Components (PCs). The deformations show various expression regions in the faces. The results indicate that Sad expression has the lowest recognition accuracy on both datasets. The classifier achieved a recognition accuracy of 99.58 and 99.32% on Stirling/ESRC and Bosphorus, respectively.
CONCLUSION: The results demonstrate that the method is robust and in agreement with the state-of-the-art results.
METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.
PURPOSE: To develop 3D personalized left ventricular (LV) models and thickening assessment framework for assessing regional wall thickening dysfunction and dyssynchrony in AMI patients.
STUDY TYPE: Retrospective study, diagnostic accuracy.
SUBJECTS: Forty-four subjects consisting of 15 healthy subjects and 29 AMI patients.
FIELD STRENGTH/SEQUENCE: 1.5T/steady-state free precession cine MRI scans; LGE MRI scans.
ASSESSMENT: Quantitative thickening measurements across all cardiac phases were correlated and validated against clinical evaluation of infarct transmurality by an experienced cardiac radiologist based on the American Heart Association (AHA) 17-segment model.
STATISTICAL TEST: Nonparametric 2-k related sample-based Kruskal-Wallis test; Mann-Whitney U-test; Pearson's correlation coefficient.
RESULTS: Healthy LV wall segments undergo significant wall thickening (P 50% transmurality) underwent remarkable wall thinning during contraction (thickening index [TI] = 1.46 ± 0.26 mm) as opposed to healthy myocardium (TI = 4.01 ± 1.04 mm). For AMI patients, LV that showed signs of thinning were found to be associated with a significantly higher percentage of dyssynchrony as compared with healthy subjects (dyssynchrony index [DI] = 15.0 ± 5.0% vs. 7.5 ± 2.0%, P
METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.
RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.
CONCLUSIONS: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.