Infection level identification for leukemia detection using optimized Support Vector Neural Network

ABSTRACT

Leukemia is the abnormal and uncontrolled development of the white blood cells, known as leukocytes, in the blood. The manual methods used for counting the blast cells have some demerits, and so automatic method must be employed. This paper proposes the Salp Swarm integrated Dolphin Echolocation-based Support Vector Neural Network (SSDE-SVNN) classifier to detect leukemia in its early stages. The pre-processed blood smear image is subjected to segmentation with the use of LUV transformation and Adaptive thresholding. The features, such as area, shape, texture, and empirical mode decomposition are extracted from the segments. The proposed classifier is used for the counting of blast cells based on the extracted features. The accuracy, specificity, and sensitivity of the proposed classifier are obtained as 0.97, 0.97, and 1, respectively, and the Mean Square Error (MSE) is noted as 0.1272.

KEYWORDS:

• Support Vector Neural Network
• leukemia
• blood smear images
• Adaptive thresholding
• blast cells
• Salp Swarm Algorithm
• Dolphin Echolocation algorithm
• LUV Transformation

Acknowledgement

The authors would like to thanks to Dr. Rupama Chaudhuri, DNB (Pathology), CMO, FWC for verification of the results by visual inspection and valuable inputs during research activity. Authors would also like to thank Mr. Swarnava Das and Mr. Krishna Kumar Jha for their support to do the task.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Biplab Kanti Das received B.Sc. degree from University of Tripura, India and the Master of Computer Application [MCA] from Allahabad Agricultural Institute, UP, India, and M.Tech (IT) from Jadavpur University, Kolkata, West Bengal, India, and pursuing Ph.D. in Computer Science & Engineering from Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India. He is presently working as Assistant Professor at MCA Department of Calcutta Institute of Technology, Howrah, West Bengal, India. His research areas include Medical Image Processing, Courseware Engineering. He has published 03 research papers in various international journals and conferences and reviewed papers for reputed Journals. He has also published 4 books for schools and colleges.

Himadri Sekhar Dutta received his B.Tech degree in Electronics and Communication Engineering from Kalyani Government Engineering College, Kalyani, India, M.Tech. degree in Optics and Opto-Electronics from University of Calcutta,Kolkata, India and Ph.D. in Technology from Institute of Radio Physics and Electronics, Kolkata, India respectively. He is presently working as Assistant Professor at ECE Department of Kalyani Government Engineering College, Kalyani. He was the Chairperson of IEEE Young Professional, Kolkata Section for two consecutive years (in 2016 and 2017) and actively participated in different activities conducted by IEEE. His research areas include Medical Image Processing, Embedded Systems and Opto-electronic Devices. He has published more than 70 research papers in various international journals and conferences and reviewed papers for reputed Journals and international conferences.

Additional information

Funding

This research activity is financially supported by R&D Project, Memo No: 148(Sanc.)/ST/P/S&T/6G-13/2018 of Science & Technology and Biotechnology Dept., Government of West Bengal, India.