Effect of Different Threshold Levels for Binarization Method in Image Classification

Abstract

Image processing is the most preferred technique in Computer-Aided Design (CAD) studies, and therefore the enhancement of image processing plays an essential role in the advancement of technology. The primary purpose of this study is to examine the effect of fixed threshold value on images of different sizes when using the binarization method in image processing. The analyzes are made based on the change in the detection accuracy percentage of the K-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN) classification methods on the MATLAB software platform. At the same time, the effect of the binarization threshold value on images with different pixel dimensions (8x8, 16x16, 32x32, 64x64, and 128x128) are investigated. CNN classification obtained the best accuracy percentage in the used malaria disease blood cell data 97.5%, followed by k-NN with 95% and SVM with 91.5%.