CNN-RSVM: a hybrid approach for classification of poikilocytosis using convolutional neural network and radial kernel basis support vector machine

Abstract

ABSTRACT The most vital blood cells in the body are red blood cells (RBCs), which carry oxygen and nutrients to the tissues. Poikilocytosis refers to a change in the red blood cells’ normal physical shape and structure. The lack of oxygen and nutrients causes defective RBCs to suffer from health problems such as anaemia and stroke. This study proposes a hybrid CNN-SVM model for promptly and precisely classifying poikilocytosis abnormalities. The features of poikilocytosis were extracted using the proposed custom CNN (Convolutional Neural Network) model and classified using the Radial kernel basis SVM (Support Vector Machine) classifier. In comparison to the CNN model with a soft-max classifier and other benchmark work, the hybrid model produced better results. The overall accuracy of the algorithm is 98.21 %, which is higher than the traditional CNN (>5 %) and other benchmark methods. This hybrid CNN-SVM model is promising for the automatic classification of poikilocytosis abnormalities.