Optimized Support Vector Machine Using Whale Optimization Algorithm for Acute Lymphoblastic Leukemia Detection from Microscopic Blood Smear Images

Acute Lymphoblastic Leukemia (ALL) is a malignancy of White Blood Cells (WBC) originating from lymphoid cells. Hematologist detects ALL through manual inspection of Microscopic Blood Smear (MBS) images and employs standard diagnostic devices like flow cytometry. But, the manual evaluation by Hematologist is prone to diagnostic error, costly, and labor-intensive. In this paper, a computer-aided ALL detection scheme using a Whale Optimization Algorithm-based Support Vector Machine (WOA-SVM) has been proposed. The major challenges are WBC segmentation, discriminant feature extraction, and WBC classification (normal and ALL). Here, CIEL^*a^*b color-based K-means clustering with a marker-controlled watershed is utilized for WBC segmentation. 15-dimensional features obtained by combining the proposed features and existing features have been used to specify the feature set. The proposed features are 2D-Discrete Orthonormal S-Transform with weighted Principal Component Analysis, the sum of rotation invariant Local Binary Pattern with a uniform pattern, and the mean intensity of Cyan of the CMYK color model. Further, an ANOVA test has been performed to check the significant features. The features are reorganized through descending order of F-values and their covariance structure is removed using Zero Phase Component Analysis whitening. Moreover, the performance of WOA-SVM is evaluated using the feature set and established a promising result with 98.42% accuracy on ALL-IDB1 dataset. The experimental outcomes demonstrate the superiority of the proposed methodology over other comparing methodologies.