Lesion Localization and Extreme Gradient Boosting Characterization with Brain Tumor MRI Images

Raja, P. M. Siva; Ramanan, Kavita

doi:10.1007/978-981-15-0978-0_39

Cited by 4 publications

(1 citation statement)

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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%