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

July 4, 2023Open Access

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

Key Points

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.

Abstract

Introduction: Intracranial hemorrhage detection in 3D Computed Tomography (CT) brain images has gained more attention in the research community. The major issue to deal with the 3D CT brain images is scarce and hard to obtain the labelled data with better recognition results. Methods: To overcome the aforementioned problem, a new model has been implemented in this research manuscript. After acquiring the images from the Radiological Society of North America (RSNA) 2019 database, the region of interest (RoI) was segmented by employing Otsu's thresholding method. Then, feature extraction was performed utilizing Tamura features: directionality, contrast, coarseness, and Gradient Local Ternary Pattern (GLTP) descriptors to extract vectors from the segmented RoI regions. The extracted vectors were dimensionally reduced by proposing a modified genetic algorithm, where the infinite feature selection technique was incorporated with the conventional genetic algorithm to further reduce the redundancy within the regularized vectors. The selected optimal vectors were finally fed to the Bi-directional Long Short Term Memory (Bi-LSTM) network to classify intracranial hemorrhage sub-types, such as subdural, intraparenchymal, subarachnoid, epidural, and intraventricular. Results: The experimental investigation demonstrated that the Bi-LSTM based modified genetic algorithm obtained 99.40% sensitivity, 99.80% accuracy, and 99.48% specificity, which are higher compared to the existing machine learning models: Naïve Bayes, Random Forest, Support Vector Machine (SVM), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) network.

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