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The research aims to enhance the detection of intracranial hemorrhage in 3D CT images using machine learning techniques.

Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm

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The research aims to enhance the detection of intracranial hemorrhage in 3D CT images using machine learning techniques.
Acquired 3D CT images from RSNA 2019 database.
Segmented regions of interest using Otsu's thresholding method.
Implemented a modified genetic algorithm for feature selection and utilized a Bi-LSTM network for classification.
Achieved 99.40% sensitivity, 99.80% accuracy, and 99.48% specificity.
Outperformed existing machine learning models including Naïve Bayes, Random Forest, and SVM.
Demonstrated high classification efficacy for various sub-types of intracranial hemorrhage.

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Randomized trial demonstrates high accuracy in detecting intracranial hemorrhage in CT images, indicating improved diagnostic potential.

Sengupta et al. (2023) studied this question.

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