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Published on: April 13, 2013
Intracerebral hemorrhage detection on computed tomography images using a residual neural network
1Valencian International University, Valencia, Spain, and Applied Biophysics and Bioengineering Group, Simon Bolivar University, Caracas, Venezuela.
Insights
This study developed a deep learning model using ResNet-18 to accurately detect intracerebral hemorrhage (ICH) from CT scans. The AI tool achieved high accuracy, aiding faster and more reliable ICH diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neurology
Background:
- Intracerebral hemorrhage (ICH) is a severe condition with high mortality, necessitating rapid diagnosis.
- Deep learning offers advanced capabilities for analyzing medical images to detect diseases like ICH.
Purpose of the Study:
- To develop and evaluate a deep learning model (ResNet-18) for differentiating CT scans of healthy brains from those with ICH.
- To enhance the interpretability of the model's decisions using Grad-CAM visualization.
Main Methods:
- Utilized a ResNet-18 deep residual convolutional neural network for image classification.
- Employed Gradient-weighted Class Activation Mapping (Grad-CAM) for visual explanations.
- Performed 100-iteration Monte Carlo cross-validation with an 80/20 train/test split on 200 CT brain images.
Main Results:
- Achieved an average accuracy of 95.93%, specificity of 96.20%, sensitivity of 95.65%, precision of 96.40%, and F1-score of 95.91%.
- Demonstrated efficient performance with an average training time of 165.90 seconds and testing time of 1.17 seconds.
- Results are comparable to state-of-the-art methods with a simpler, less computationally intensive approach.
Conclusions:
- The developed deep learning detector shows high efficacy in identifying ICH from CT images.
- The model can serve as a valuable tool for physicians, improving diagnostic speed, accuracy, and resource allocation.
- This approach offers a computationally efficient method for ICH detection, supporting clinical decision-making.
Abstract:
Intracerebral hemorrhage (ICH) is a high mortality rate, critical medical injury, produced by the rupture of a blood vessel of the vascular system inside the skull. ICH can lead to paralysis and even death. Therefore, it is considered a clinically dangerous disease that needs to be treated quickly. Thanks to the advancement in machine learning and the computing power of today's microprocessors, deep learning has become an unbelievably valuable tool for detecting diseases, in particular from medical images. In this work, we are interested in differentiating computer tomography (CT) images of healthy brains and ICH using a ResNet-18, a deep residual convolutional neural network. In addition, the gradient-weighted class activation mapping (Grad-CAM) technique was employed to visually explore and understand the network's decisions. The generalizability of the detector was assessed through a 100-iteration Monte Carlo cross-validation (80% of the data for training and 20% for test). In a database with 200 CT images of brains (100 with ICH and 100 without ICH), the detector yielded, on average, 95.93%accuracy, 96.20% specificity, 95.65% sensitivity, 96.40% precision, and 95.91% F1-core, with an average computing time of 165.90 s to train the network (on 160 images) and 1.17 s to test it with 40 CT images. These results are comparable with the state of the art with a simpler and lower computational load approach. Our detector could assist physicians in their medical decision, in resource optimization and in reducing the time and error in the diagnosis of ICH.
