"CNN-SVM Hybrid Model for Epilepsy Seizure Detection from MRI Images" - Nagaraja S. R., Riyazulla Rahman J (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Epilepsy is a chronic disorder characterized by recurrent seizures, which affects around 50 million people worldwide. Early detection of seizures through analysis of medical images can allow for timely treatment and improved outcomes. In this paper, we develop a hybrid machine learning approach that combines a support vector machine (SVM) and a convolutional neural network (CNN) for automated epilepsy seizure detection from magnetic resonance imaging (MRI) scans. The model uses the SVM as a classifier, with kernel functions based on deep features extracted from the MRI images by the CNN. The CNN encodes useful representations of the spatial structure in the images to better differentiate between healthy brain scans and those showing epileptiform discharges. The SVM then uses these deep features to classify each scan as either seizure or non-seizure. We evaluate the model on two datasets of MRI scans, from epilepsy patients experiencing seizures. Using 5-fold cross-validation, our proposed SVM-CNN system achieves a accuracy over 98.74% in detecting seizures, outperforming previous benchmarks. The hybrid integration of shallow and deep learning methods allows for interpretable seizure detection while enhancing accuracy. This diagnostic aid can facilitate earlier administration of anti-epileptic treatment and contribute positively to patient outcomes.