Multi-modal Feature Fusion with Machine Learning Approach for Leukemia Detection and Classification - I. Vinurajan (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Leukemia is a significant cause of death worldwide and is a fatality group of cancer-related disorder that affects all age groups, mainly children and grownups. Mostly, it is related to White Blood Cells (WBC), which is supplemented by an increase in the diverse range of immature lymphocytes and affect negatively the bone marrow or bloodstream. As a result, a reliable and rapid cancer analysis is a fundamental prerequisite for effective treatment to increase the survival rate. At present, a manual diagnosis of blood samples attained by microscopic imageries is done to detect diseases that are time-consuming, less accurate, and often very slow. Moreover, the shape and appearance of leukemic cells look like ordinary cells which make detection challenging, in microscopic analysis. Currently, machine learning (ML) methods have become a better method for medical image analysis. This study presents a Multi-modal Feature Fusion with Machine Learning for Leukemia Detection and Classification (MMFFML-LDC) technique. The MMFFML-LDC technique mostly proposes to identify and categorize the occurrence of leukemia on microscopic blood images. In the MMFFML-LDC system, an initial phase of pre-processing is involved in two levels: median filtering (MF) based noise removal and adaptive histogram equalization (AHE) based contrast enhancement. Furthermore, watershed segmentation is employed to segment the pre-processed images. For feature extraction, a fusion of four ML feature extractors namely histogram of gradients (HOG), local binary pattern (LBP), scale-invariant feature transform (SIFT), and gray level co-occurrence matrix (GLCM). Finally, the detection of leukemia can be performed by the usage of a support vector machine (SVM). The performance analysis of the MMFFML-LDC technique can be studied using a blood image dataset from Kaggle. The experimental values are definite that the MMFFML-LDC system obtains better performance over other ML classifiers.