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Automated detection of polymicrogyria in pediatric patients using deep learning
Shagnik Guha1, Venkatesh Bhandage2, Aman Agarwal1
1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Scientific Reports
|November 25, 2025
Summary
Advanced image preprocessing significantly improves Convolutional Neural Network (CNN) performance for diagnosing polymicrogyria (PMG) from MRI scans. This technique enhances subtle feature recognition, aiding clinicians in identifying this complex neurological disorder.
Area of Science:
- Neurology
- Medical Imaging
- Artificial Intelligence
Background:
- Polymicrogyria (PMG) is a complex neurological disorder characterized by abnormal cortical folding, often leading to developmental delays and seizures.
- Subtle imaging features of PMG can present diagnostic challenges, even for experienced clinicians.
- Accurate and timely diagnosis of PMG is crucial for effective patient management and intervention.
Purpose of the Study:
- To evaluate the impact of advanced image preprocessing techniques on the diagnostic performance of Convolutional Neural Networks (CNNs) for polymicrogyria (PMG).
- To identify optimal preprocessing strategies for enhancing the detection of subtle PMG-related abnormalities in brain MRI scans.
- To improve the accuracy and reliability of AI-assisted PMG diagnosis.
Main Methods:
- A preprocessing pipeline including Min-Max normalization, CLAHE, Bilateral filtering, and Canny edge detection was applied to brain MRI scans.
- Multiple CNN architectures (ResNet-101, ResNet, VGG, MobileNetV2, DenseNet-201) were trained and evaluated with and without preprocessing.
- GradCAM++ was utilized for visualizing and interpreting the decision-making processes of the CNN models.
Main Results:
- All tested CNN architectures showed performance enhancements after image preprocessing.
- ResNet-101 achieved the most significant accuracy improvement, increasing by 10.3%.
- ResNet and VGG architectures demonstrated superior performance gains compared to MobileNetV2 and DenseNet-201.
Conclusions:
- Advanced image preprocessing techniques are critical for enhancing the capabilities of deep learning models in medical image analysis.
- The proposed preprocessing strategy effectively improves the detection of subtle abnormalities associated with polymicrogyria.
- This methodology offers a valuable tool to assist healthcare providers in the accurate diagnosis of polymicrogyria.
