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Exemplar deep and hand-modeled features based automated and accurate cerebral hemorrhage classification method
M Sait Din1, Sukru Gurbuz1, Erhan Akbal2
1Department of Emergency, College of Medicine, Inonu University, Malatya, Turkey.
Medical Engineering & Physics
|July 5, 2022
Summary
A machine learning model accurately classifies cerebral hemorrhage (CH) types from CT scans using fused features. This approach enhances diagnostic speed and accuracy for emergency medical services.
Area of Science:
- Medical Imaging
- Machine Learning
- Neurology
Background:
- Cerebral hemorrhage (CH) diagnosis is critical for effective treatment.
- Prompt and accurate CH classification is essential in clinical settings.
Purpose of the Study:
- To develop an accurate machine learning model for classifying cerebral hemorrhage (CH) types.
- To simplify the diagnostic process for CH.
Main Methods:
- Utilized a dataset of 9818 CT images across five categories.
- Developed an exemplar fused feature generator combining AlexNet, LBP, and LPQ.
- Employed Neighborhood Component Analysis (NCA) for feature selection and Support Vector Machine for classification.
Main Results:
- The model achieved high accuracies, including 97.47% with five hold-out validations and 96.34% with ten-fold cross-validation.
- Demonstrated robust performance across multiple validation methods.
Conclusions:
- The developed model and feature generation technique are successful and robust.
- This model can significantly aid emergency services in prompt CH diagnosis.
