Innovative Feature Extraction and Machine Learning Joint Approaches for Automated Detection of Focal Cortical Dysplasia Type II

Innovative Feature Extraction and Machine Learning Joint Approaches for Automated Detection of Focal Cortical Dysplasia Type II

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Focal cortical dysplasia type II (FCD II) is an epileptogenic lesion often associated with pharmacoresistant epilepsy.Yet, owing to their subtle appearance, radio birdman tshirt identifying these lesions via 3D Magnetic Resonance Image (MRI) remains a complex challenge, rendering them susceptible to evasion by conventional visual analysis.In this study, we consider advancing a novel extraction featuring a volumetric approach dubbed Volumetric Decimal Descriptor Pattern (V-DDP), whereby, data volumetric representations can be effectively captured, providing a comprehensive depiction of the spatial relationships and structural nuances within the dataset.Thus, by applying this unique feature extraction read more approach, we have been able to decipher more significant information and unlock a relatively rich context, paving the way for a greater recognition scope of such nuanced patterns.This approach has been upheld by three classifiers, namely, the k-nearest neighbors (KNN), the Linear Discriminant Analysis (LDA) and the Support Vector Machine (SVM).

Our experimental results demonstrate the significant effectiveness of the proposed approach with the nonlinear SVM classifier.We significantly outperform the state-of-the-art models, especially, in complex volumetric data bound areas.