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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.
Abstract:
Polymicrogyria (PMG) is a multifaceted neurological disorder caused by abnormal cortical folding, mostly in children. It commonly results in developmental delays, seizures, and motor weakness. The mild features of PMG in neuroimaging often make its identification difficult, even for experts. In this paper, we assess the efficacy of various advanced image preprocessing strategies on the overall performance of Convolutional Neural Network (CNN) applied for PMG diagnosis in MRI brain scans. We employ a pre-processing sequence that includes Min-Max normalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), Bilateral filtering, and Canny edge detection aimed at improving the recognition of subtle features without losing essential details. The techniques can enhance the visualization of delicate structural deformities in the brain MRI images and assist in the diagnosis of neurological disorders by clinicians. Experimental results suggest that performance enhancement was achieved with all of the tested CNN architectures. ResNet-101 has exhibited the most remarkable accuracy enhancement by 10.3%. ResNet and VGG architectures delivered much greater performance improvement as compared to MobileNetV2 and DenseNet-201 models. GradCAM++ is adopted to infer the decision-making mechanism of the considered deep learning architectures. The methodology finds applications in neurological imaging and may be used to assist healthcare providers in the diagnosis of polymicrogyria. Our findings emphasize the crucial role of image pre-processing techniques in increasing the capabilities of deep learning frameworks to assist with complex tasks in medical image analysis.
Insights
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.
