Segmentation of Basal Nuclei and Anatomical Brain Structures Using Support Vector Machines

Bosnjak, Antonio; Villegas, Ricardo Astudillo; Montilla, G.; Jara, I.

doi:10.1007/978-3-319-00846-2_80

Cited by 2 publications

(2 citation statements)

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“…32 variables are selected out of 42 for analysis using 10 fold crossvalidation on the training set A. To predict the mortality rate of the ICU patient's 42 has developed SVM model. During the preprocessing, each variable's mean, standard deviation, min and max value are extracted for further analysis.…”

Section: Introductionmentioning

confidence: 99%

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2023

IJPSR

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Predicting ICU patient's mortality is an important area of research to assist the clinical staff in decision-making, and subsequently make more exact strategies in recognizing high mortality risk patients. Handling huge clinical data is still a challenge. These data have different issues such as high dimensions, many missing values, imbalanced data, timeseries and data recorded irregularly. This paper mainly focuses on developing four different deep learning models: deep neural network (DNN), deep long-short term memory (DLSTM), deep bidirectional long-short term memory (DBLSTM) and deep gated recurrent unit (DGRU) using two standard datasets, the Physionet challenge 2012 and WiDS datathon 2020 to predict the ICU patients mortality. During the simulation study, missing values are handled using k-NN imputation in Physionet and mean imputation in WiDSdatathon, then balancing of the data is done by employing synthetic minority oversampling technique (SMOTE), cost sensitive learning (CSL) and generative adversarial network (GAN). Feature extraction is done by using discrete wavelet transform (DWT) in WiDS datathon. From the simulation study, it is demonstrated that the (SMOTE+DGRU) has obtained AUC, F1-score and accuracy of 0.8081, 0.7964, 0.8081 respectively in 90 epochs for Physionet challenge 2012. Whereas (SMOTE+DBLSTM) has provided AUC, F1-score and accuracy of 0.8724, 0.8739, 0.8724 respectively for same epochs for WiDSdatathon. In overall, it is observed that the SMOTE balancing technique is performing better in comparison to CSL and GAN.

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Section: Introductionmentioning

confidence: 99%

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2023

IJPSR

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“…Researchers have developed all kinds of methods for image segmentation [9][10][11][12][13], but most of the traditional methods are limited by the complex and imprecise structures, motion noise, weak boundaries and partial volume effects of the majority of medical images [2,14,15]. There are three mainstream methods in image segmentation-the edge detection method, region-based method and hybrid method-and each of them has its respective market [16].…”

Section: Introductionmentioning

confidence: 99%

A new method based on Dempster–Shafer theory and fuzzy c-means for brain MRI segmentation

Liu

et al. 2015

Meas. Sci. Technol.

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In this paper, a new method is proposed to decrease sensitiveness to motion noise and uncertainty in magnetic resonance imaging (MRI) segmentation especially when only one brain image is available. The method is approached with considering spatial neighborhood information by fusing the information of pixels with their neighbors with Dempster–Shafer (DS) theory. The basic probability assignment (BPA) of each single hypothesis is obtained from the membership function of applying fuzzy c-means (FCM) clustering to the gray levels of the MRI. Then multiple hypotheses are generated according to the single hypothesis. Then we update the objective pixel’s BPA by fusing the BPA of the objective pixel and those of its neighbors to get the final result. Some examples in MRI segmentation are demonstrated at the end of the paper, in which our method is compared with some previous methods. The results show that the proposed method is more effective than other methods in motion-blurred MRI segmentation.

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Segmentation of Basal Nuclei and Anatomical Brain Structures Using Support Vector Machines

Cited by 2 publications

References 8 publications

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A new method based on Dempster–Shafer theory and fuzzy c-means for brain MRI segmentation

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