Brain Image Classification Using Optimized Extreme Gradient Boosting Ensemble Classifier

Das, Abhishek; Mohapatra, Saumendra Kumar; Mohanty, Mihir Narayan

doi:10.1007/978-981-16-8739-6_20

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The new test observation will be classified to +1 class if the weighted average obtained is positive otherwise it will be classified to -1 class. Some of the recent works that utilized this method for classification problems associated with brain imaging analysis are listed here [32,33,34,35].…”

Section: Gradient Boostingmentioning

confidence: 99%

Identifying the Neurocognitive Difference Between Two Groups Using Supervised Learning

Rimal

2023

Stat., optim. inf. comput.

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Brain Imaging Analysis is a dynamic and exciting field within neuroscience. This study is conducted with two main objectives. First, to develop a classification framework to enhance predictive performance, and second, to conduct a comparative analysis of accuracy versus inference using brain imaging data. The dataset of chess masters and chess novices is utilized to identify neurocognitive differences between the two groups, based on their resting-state functional magnetic resonance imaging data. A network of connections between brain regions is created and analyzed. Standard statistical learning techniques and machine learning models are then applied to distinguish connectivity patterns between the groups. The trade-off between model precision and interpretability is also assessed. Finally, model performance measures, including accuracy, sensitivity, specificity, and AUC, are reported to demonstrate the effectiveness of the model framework.

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Section: Gradient Boostingmentioning

confidence: 99%

Identifying the Neurocognitive Difference Between Two Groups Using Supervised Learning

Rimal

2023

Stat., optim. inf. comput.

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“…The damaged-area evaluation has been proposed in [ 29 ] using the Gauss derivation theorem that provides a new direction in brain MRI processing. Parameter control-based optimization technique have been utilized for brain tumor detection using extreme gradient boosting ensemble model [ 30 ]. Ensemble learning-based models are gaining the attention.…”

Section: Introductionmentioning

confidence: 99%

Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

Mohanty¹,

Das

Mohanty

et al. 2022

Healthcare

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Background: The modern era of human society has seen the rise of a different variety of diseases. The mortality rate, therefore, increases without adequate care which consequently causes wealth loss. It has become a priority of humans to take care of health and wealth in a genuine way. Methods: In this article, the authors endeavored to design a hospital management system with secured data processing. The proposed approach consists of three different phases. In the first phase, a smart healthcare system is proposed for providing an effective health service, especially to patients with a brain tumor. An application is developed that is compatible with Android and Microsoft-based operating systems. Through this application, a patient can enter the system either in person or from a remote place. As a result, the patient data are secured with the hospital and the patient only. It consists of patient registration, diagnosis, pathology, admission, and an insurance service module. Secondly, deep-learning-based tumor detection from brain MRI and EEG signals is proposed. Lastly, a modified SHA-256 encryption algorithm is proposed for secured medical insurance data processing which will help detect the fraud happening in healthcare insurance services. Standard SHA-256 is an algorithm which is secured for short data. In this case, the security issue is enhanced with a long data encryption scheme. The algorithm is modified for the generation of a long key and its combination. This can be applicable for insurance data, and medical data for secured financial and disease-related data. Results: The deep-learning models provide highly accurate results that help in deciding whether the patient will be admitted or not. The details of the patient entered at the designed portal are encrypted in the form of a 256-bit hash value for secured data management.

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Classification of the Speech Signal of Parkinson's Patient using Optimized Ensemble Model

Mohapatra

Ram

Alamuru³

et al. 2023

2023 International Conference for Advancement in Technology (ICONAT)

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Brain Image Classification Using Optimized Extreme Gradient Boosting Ensemble Classifier

Cited by 3 publications

References 14 publications

Identifying the Neurocognitive Difference Between Two Groups Using Supervised Learning

Identifying the Neurocognitive Difference Between Two Groups Using Supervised Learning

Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

Classification of the Speech Signal of Parkinson's Patient using Optimized Ensemble Model

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