Optimized H-StrokeNet: A Deep Learning Network for Stroke Classification

Hybrid classifiers are neural network models that are structured with two different network architectures to create the best deep learning model.The hybrid models are developed generally to meet certain complex feature learning problems.However, the nature of hybrid classifiers causes the network nodes to learn 'n' number of features, which increases the computational complexity of the classifiers.Therefore, the implementation of a hybrid classifier reaches an impossible state when the processor and memory spaces are restricted in real-time applications.The proposed work utilizes the feedforward neural network architecture with two hidden layers in combination with the eXtreme Gradient Boosting (XGBoost) algorithm to form a hybrid classifier called H-StrokeNet, and a Crow Search Optimization (CSO) algorithm is included in the work to reduce the complexity of the hybrid network by providing optimized feature extraction.The performance of the proposed work is verified with the regular XGBoost algorithm and deep neural networks using the Laboratory of Processing Image, Signals, and Computer Science (LAPISCO) dataset, and a comparative analysis is made with different algorithms such as support vector machine (SVM), Decision Tree (DT), Na ve Bayes (NB), k-Nearest Neighbor (kNN), and XGBoost.Results show the superior performance of the proposed system with an overall accuracy of 96.55%, whereas it is 92.09% with the XGBoost classifier.