An Effective Approach for Sub-acute Ischemic Stroke Lesion Segmentation by Adopting Meta-Heuristics Feature Selection Technique Along with Hybrid Naive Bayes and Sample-Weighted Random Forest Classification

Babu, Melingi Sunil; Vijayalakshmi, V.

doi:10.1007/s11220-019-0230-6

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…There are three accelerometer channels (x-, y-, z-axes), three gyroscope channels (x-, y-, z-axes), and one sEMG channel for each module. Thirteen common features were extracted for each channel, including mean, median, variance, absolute energy, root mean square, standard deviation, skewness, kurtosis, zero crossing, sample entropy, slope sign changes, interquartile range, mean absolute value [ 30 , 31 , 32 , 33 , 34 ]. Two complementary features (maximum jerk and mean jerk) [ 34 , 35 ] were extracted for accelerometers.…”

Section: Materials and Methodsmentioning

confidence: 99%

Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Meng

An-jing

Chen

et al. 2021

Sensors

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Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris.

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Section: Materials and Methodsmentioning

confidence: 99%

Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Meng

An-jing

Chen

et al. 2021

Sensors

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“…An abnormal‐to‐normal translation generative adversarial network was proposed by Sunil Babu and Vijayalakshmi 6 to transform a medical image with lesions into a matched image with the lesion “removed.” A CNN method based on DL was created by Asif et al 29 to recognize a brain tumor from an MRI. By using bigger datasets and other deep‐learning techniques, the system's performance can be further improved.…”

Section: Related Workmentioning

confidence: 99%

“…MRI is one of the most frequently used medical imaging methods. 5,6 The information obtained by an MRI is especially useful for finding brain tumors. 7 Brain tumor identification requires highly accurate segmentation of MRI brain images.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based robust hybrid approaches for brain tumor classification in magnetic resonance images

Krishnasamy,

Ponnusamy

2023

Int J Imaging Syst Tech

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The classification of brain images is an immensely challenging task and one of the most valuable and frequently employed procedures in the field of medical diagnostics. Deep Learning (DL), which falls under the umbrella of artificial intelligence, has introduced novel techniques for automated analysis of medical images. The objective of this study was to develop two hybrid DL models for the segmentation and classification of brain magnetic resonance imaging (MRI) data. The first hybrid model combines fully convolutional networks (FCNs) and residual networks (ResNets) for both segmentation and classification purposes. The second model is a hybrid of SegNet for segmentation and MobileNet for classification. Rest assured, it will provide you with a dependable and promising performance. The analysis was conducted on a labeled dataset comprising images of glioma, meningioma, pituitary, and no‐tumor cases. Python was utilized to implement the aforementioned hybrid models. The proposed models achieved remarkable accuracies of 93.9% and 91.3%, respectively, when evaluated on a brain tumor MRI dataset. Additionally, a comprehensive evaluation of the proposed models was conducted, comparing them with other existing hybrid models in terms of metrics such as the dice score, Jaccard index, positive predictive value (PPV), false predictive value (FPV), testing time, precision, recall, specificity, and F1 score. The results demonstrated that the proposed DL technique can significantly assist doctors and radiologists in the early detection of brain tumor cells.

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“…The weights are assigned to every tree and can be viewed as training parameters which are computed by solving a standard quadratic optimization problem maximizing Harrell's C-index. Finally, Sunil Babu et al [25] utilized an effective meta-heuristic feature selection technique along with hybrid Naive Bayes (NB) and sample weighted Random Forest (SWRF) classification approach for sub-acute ischemic stroke lesion segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

Weighted Random Forests to Improve Arrhythmia Classification

et al. 2020

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Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model.

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An Effective Approach for Sub-acute Ischemic Stroke Lesion Segmentation by Adopting Meta-Heuristics Feature Selection Technique Along with Hybrid Naive Bayes and Sample-Weighted Random Forest Classification

Cited by 17 publications

References 28 publications

Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Deep learning‐based robust hybrid approaches for brain tumor classification in magnetic resonance images

Weighted Random Forests to Improve Arrhythmia Classification

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