Open Access

ARTICLE

Automatic Leukaemia Segmentation Approach for Blood Cancer Classification Using Microscopic Images

by Anuj Sharma¹, Deepak Prashar², Arfat Ahmad Khan³, Faizan Ahmed Khan⁴, Settawit Poochaya^3,*

1 School of Computer Applications, Lovely Professional University, Phagwara, 144411, India
2 School of Computer Science & Engineering, Lovely Professional University, Phagwara, 144411, India
3 Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
4 COMSATS University Lahore, 54000, Pakistan

* Corresponding Author: Settawit Poochaya. Email:

Computers, Materials & Continua 2022, 73(2), 3629-3648. https://doi.org/10.32604/cmc.2022.030879

Received 04 April 2022; Accepted 06 May 2022; Issue published 16 June 2022

Abstract

Leukaemia is a type of blood cancer that is caused by undeveloped White Blood Cells (WBC), and it is also called a blast blood cell. In the marrow of human bones, leukaemia is developed and is responsible for blood cell generation with leukocytes and WBC, and if any cell gets blasted, then it may become a cause of death. Therefore, the diagnosis of leukaemia in its early stages helps greatly in the treatment along with saving human lives. Subsequently, in terms of detection, image segmentation techniques play a vital role, and they turn out to be the important image processing steps for the extraction of feature patterns from the Acute Lymphoblastic Leukaemia (ALL) type of blood cancer. Moreover, the image segmentation technique focuses on the division of cells by segmenting a microscopic image into background and cancer blood cell nucleus, which is well-known as the Region Of Interest (ROI). As a result, in this article, we attempt to build a segmentation technique capable of solving blood cell nucleus segmentation issues using four distinct scenarios, including K-means, FCM (Fuzzy C-means), K-means with FFA (Firefly Algorithm), and FCM with FFA. Also, we determine the most effective method of blood cell nucleus segmentation, which we subsequently use for the Leukaemia classification model. Finally, using the Convolution Neural Network (CNN) as a classifier, we developed a leukaemia cancer classification model from the microscopic images. The proposed system’s classification accuracy is tested using the CNN to test the model on the ALL-IDB dataset and equate it to the current state of the art. In terms of experimental analysis, we observed that the accuracy of the model is near to 99%, and it is far better than other existing models that are designed to segment and classify the types of leukaemia cancer in terms of ALL.

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