Medical Image Classification Using Different Machine Learning Algorithms

Sami Ismael,Shahab Kareem,Firas Almukhtar

doi:10.33899/csmj.2020.164682

Sami Ismael, Shahab Kareem + Show 1 more

Open Access

https://doi.org/10.33899/csmj.2020.164682

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Abstract

The different types of white blood cells equips us an important data for diagnosing and identifying of many diseases. The automation of this task can save time and avoid errors in the identification process. In this paper, we explore whether using shape features of nucleus is sufficient to classify white blood cells or not. According to this, an automatic system is implemented that is able to identify and analyze White Blood Cells (WBCs) into five categories (Basophil, Eosinophil, Lymphocyte, Monocyte, and Neutrophil). Four steps are required for such a system; the first step represents the segmentation of the cell images and the second step involves the scanning of each segmented image to prepare its dataset. Extracting the shapes and textures from scanned image are performed in the third step. Finally, different machine learning algorithms such as (K* classifier, Additive Regression, Bagging, Input Mapped Classifier, or Decision Table) is separately applied to the extracted (shapes and textures) to obtain the results. Each algorithm results are compared to select the best one according to different criteria’s.

Highlights

In medicine, further especially those areas of hematology and dangerous infections, classifying various types of blood cells can be utilized as tools in the medical analysis, by counting the repetitions of individual cells and match it with the number taken from the normal blood samples
Jaroonrut and Charnchai [2] proposed a method that involves a classification, feature extraction, nucleus segmentation, pre-processing, feature selection, and cell segmentation to decide about identifying the blood ailments
A mathematical morphological process are used to extract some elements not considered as White Blood Cells (WBCs), shape-based characteristics are selected to classify them into different categories

Summary

Introduction

Further especially those areas of hematology and dangerous infections, classifying various types of blood cells can be utilized as tools in the medical analysis, by counting the repetitions of individual cells and match it with the number taken from the normal blood samples. Jaroonrut and Charnchai [2] proposed a method that involves a classification, feature extraction, nucleus segmentation, pre-processing, feature selection, and cell segmentation to decide about identifying the blood ailments. Siddhartha and Bibek [5] propose a computerized system that can use several image processing techniques to recognize the white blood cell found in a microscopic image of a human blood samplewith the aid of Neural Network methods. A dataset with hundreds of images of white blood cells are examined and classified depending on several methods. A special enhanced algorithm is used to extract and segment the WBC, machine learning algorithms are utilized for the classification process to get the ultimate results. The final section will clarify the conclusions of this study followed by references

Image Preprocessing

Classification Algorithms

Bagging

Input Mapped Classifier

Algorithms and Experiments

Experimental Results