Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neurology

Background:

• Evaluating the impact of sample size on developing 3D convolutional neural network (3DCNN) models for intracranial hemorrhage (ICH) detection.
• Investigating the binary classification of intraparenchymal, subarachnoid, and subdural hemorrhage (IPH, SAH, SDH).

Purpose of the Study:

• To assess how varying sample sizes affect the performance of 3DCNN models in classifying different types of ICH.
• To determine the minimum sample size required for reliable AI model development in ICH detection.

Main Methods:

• Compiled brain CT scan images into 3D images for 3DCNN model training.
• Varied non-hemorrhage and ICH case numbers to test model performance.
• Utilized cross-validation to compute average area under the curve (AUC) and accuracy for model evaluation.

Main Results:

• Larger sample sizes resulted in stable and acceptable AI model performance.
• Training with limited cases risked falsely high AUC values or accuracy.
• Model performance varied across ICH types (IPH, SAH, SDH), with more stability observed at larger sample sizes.

Conclusions:

• 3DCNN models can detect ICH even with limited data, but a minimum case number is essential.
• AI model performance for ICH detection is sample size-dependent and varies by hemorrhage type.
• Larger datasets enhance the stability and reliability of AI models for diagnosing intracranial hemorrhages.