Open Access

ARTICLE

Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

by G. Sriram¹, T. R. Ganesh Babu², R. Praveena^2,*, J. V. Anand³

1 Research Scholar, Muthayammal Engineering College, Rasipuram, Namakkal, Tamil Nadu, 637408, India
2 Muthayammal Engineering College, Rasipuram, Namakkal, Tamil Nadu, 637408, India
3 Siddartha Institute of Science and Technology, Puttur, Andhra Pradesh, 517583, India

* Corresponding Author: R. Praveena. Email:

Molecular & Cellular Biomechanics 2022, 19(1), 29-40. https://doi.org/10.32604/mcb.2022.016966

Received 15 April 2021; Accepted 27 September 2021; Issue published 12 January 2022

Abstract

Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts is performed to support classification of leukemia and leukemoid reactions. Leukocytosis is commonly observed both in leukemia and leukemoid hence physicians may have chance of wrong diagnosis of malignant leukemia for the patients with leukemoid reactions. BCCD (blood cell count detection) Dataset has been used which has 364 blood smear images of which 349 are of single WBC type. The Image segmentation algorithm of Hue Saturation Value color based on watershed has been applied. VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images. The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.

---

Keywords

---