Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning

Abstract

Identification and diagnosis of leukemia earlier is a contentious issue in therapeutic diagnostics for reducing the rate of death among people with Acute Lymphoblastic Leukemia (ALL). The investigation of White Blood Cells (WBCs) is essential for the detection of ALL-leukaemia cells, for which blood smear images were being used. This study created an intelligent framework for identifying healthy blood cells from leukemic blood cells in blood smear images. The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)). As the training set, the ALL-IDB2 database was utilized to create a balanced database with 260 blood smear images. Consequently, to generate the optimum feature set, a recommended model was established by using numerous individual and combined feature extraction methodologies. The investigational consequences demonstrate that the developed feature fusion strategy surpassed previous existing techniques, with an overall accuracy of 97.49 ± 1.02% utilizing Ensemble classifier.

BwMMV-pred: A Novel Ensemble learning approach using blood smear images for Malaria Prediction

“…Traditionally, the texture features extraction has been successfully used by local binary pattern [33] [34]. Motivated by these studies, we propose to use the local binary pattern (LBP) [35] to attain the feature set of blood smear images.…”

“…A flow diagram of hard voting is shown in Figure 4. Motivated by previous studies [33], we propose to use the local binary pattern (LBP) to attain the feature set of blood smear images. Essentially, the value of each pixel is computed by considering a 3×3 neighborhood.…”