Feasible Study on Intracranial Hemorrhage Detection and Classification using a CNN-LSTM Network

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Radiology

Background:

• Intracranial hemorrhage (ICH) is a critical condition with diverse causes, including stroke, trauma, and hypertension.
• Accurate and timely identification of ICH subtypes is crucial for patient outcomes.
• Current diagnostic methods rely on manual interpretation of head CT scans, which can be time-consuming.

Purpose of the Study:

• To develop and assess a deep learning model for the automatic identification and classification of intracranial hemorrhage (ICH) subtypes.
• To evaluate the feasibility of using head CT images for AI-driven ICH diagnosis.
• To enhance the accuracy and efficiency of ICH detection and classification in clinical practice.

Main Methods:

• A deep learning approach utilizing a CNN-LSTM model was employed for ICH classification.
• The model was trained on 4,516,842 head CT images after applying windowing techniques (brain, bone, subdural).
• The Xception model served as the deep CNN backbone, with LSTM configured with 64 nodes and 32 timesteps.

Main Results:

• The model was tested on 727,392 head CT images.
• The automated system achieved a weighted multi-label logarithmic loss of 0.07528.
• The proposed method demonstrated high accuracy in identifying and classifying ICH subtypes.

Conclusions:

• The developed deep learning technique shows significant potential for the automatic identification and classification of ICH.
• This AI-driven approach can assist radiologists in interpreting head CT scans, improving diagnostic accuracy.
• The method offers a valuable tool for quantitative analysis in neuroimaging and brain-related conditions.