Convolutional Neural Network-Based Automated Acute Lymphoblastic Leukaemia Detection and Stage Classification from Peripheral Blood Smear Images

Deepanshu ¹ , K. Srinivas² , A. Charan Kumari³

Section:Research Paper, Product Type: Journal-Paper
Vol.12 , Issue.3 , pp.21-28, Jun-2024

Online published on Jun 30, 2024

Copyright © Deepanshu, K. Srinivas, A. Charan Kumari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
Acute Lymphoblastic Leukaemia (ALL) poses significant challenges in diagnosis and treatment due to its high mortality rates and complex subtyping. In this study, ALLNet, a Convolutional Neural Networks (CNN) is employed to automate ALL detection using a publicly available dataset of microscopic peripheral blood smear (PBS) images. The ALLNet model demonstrates good performance, achieving an accuracy of 92% after 70 epochs of training. Through extensive evaluation using classification reports and confusion matrices, the model`s ability to differentiate between four classes of Benign, Early, Pre, and Pro have been analysed. The accuracy, recall, precision, and F1-score metrics for each class indicate robust performance, with particularly high values for the `Pro` class, suggesting the model`s efficacy in capturing nuanced patterns indicative of different leukaemia subtypes. Furthermore, the investigation highlights ALLNet‘s consistency across the dataset, effectively minimising both false positives and false negatives. These findings underscore the potential of CNNs in medical image analysis, particularly in the domain of leukaemia classification and detection.

Key-Words / Index Term :
Medical Image Analysis, Acute Lymphoblastic Leukaemia, Convolutional Neural Network, Deep Learning, Blood Smear Images

References :
[1] S. Agaian, M. Madhukar, A. T. Chronopoulos, “Auto- mated screening system for acute myelogenous leukaemia detection in blood microscopic images,” IEEE Systems jour- nal, Vol.8, Issue.3, pp995–1004, 2014.
[2] S. Shafique, S. Tehsin, “Acute lymphoblastic leukaemia detection and classification of its subtypes using pretrained deep convolutional neural networks,” Technology in Cancer Research & Treatment, Vol.17, 2018.
[3] A. Biondi, G. Cimino, R. Pieters, C.-H. Pui, “Biological and therapeutic aspects of infant leukaemia,” Blood, Vol.96, Issue.1, pp.24–33, 2000.
[4] R. D. Labati, V. Piuri, F. Scotti, “All-IDB: the acute lymphoblastic leukaemia image database for image processing,” In the Proceedings of the 2011 18th IEEE International Conference on Image Processing, pp.2045–2048, 2011.
[5] T. Tran, O.-H. Kwon, K.-R. Kwon, S.-H. Lee, K.-W. Kang, “Blood cell images segmentation using deep learning semantic segmentation,” In the Proceedings of the 2018 IEEE International Conference on Electronics and Communication Engineering, China, pp.13–16, 2018.
[6] T. C. Fujita, N. Sousa-Pereira, M. K. Amarante, M. A. E. Watanabe, “Acute lymphoid leukaemia etiopathogenesis,” Molecular Biology Reports, Vol.48, Issue.1, pp. 817–822, 2021.
[7] V. Singhal, P. Singh, “Local binary pattern for automatic detection of acute lymphoblastic leukaemia,” In the Proceedings of the 2014 Twentieth National Conference on Communications, India, pp.1–5, 2014.
[8] H. Mohamed, R. Omar, N. Saeed, “Automated detection of white blood cells cancer diseases,” In the Proceedings of the 2018 First International Workshop on Deep and Representation Learning, Egypt, pp.48–54, 2018.
[9] S. Mohapatra, D. Patra, S. Satpathy, “An ensemble classifier system for early diagnosis of acute lymphoblastic leukaemia in blood microscopic images,” Neural Computing & Applications, vol.24, Issue.7-8, pp.1887–1904, 2014.
[10] N. Patel, A. Mishra, “Automated leukaemia detection using microscopic images,” Procedia Computer Science, Vol.58, pp.635–642, 2015.
[11] R. Bhattacharjee, L. M. Saini, "Detection of Acute Lymphoblastic Leukaemia using watershed transformation technique," In the Proceedings of the International Conference on Signal Processing, Computing and Control, India, pp.383-386, 2015.
[12] S. Mishra, B. Majhi, and P. K. Sa, “Texture feature based classification on microscopic blood smear for acute lym- phoblastic leukaemia detection,” Biomedical Signal Processing and Control, Vol.47, pp.303–311, 2019.
[13] M. Shaheen, R. Khan, R. Biswal, “Acute myeloid leukaemia (AML) detection using AlexNet model,” Complexity, Vol.2021, 2021.
[14] A. Rehman, N. Abbas, T. Saba, S. I. u. Rahman, Z. Mehmood, H. Kolivand, “Classification of acute lymphoblastic leukaemia using deep learning,” Microscopy Research and Technique, Vol.81, Issue. 11, pp.1310–1317, 2018.
[15] M. Zakir Ullah, Y. Zheng, J. Song, “An attention-based convolutional neural network for acute lymphoblastic leu- kemia classification,” Applied Sciences, Vol.11, Issue.22, 2021.
[16] T. Pansombut, S. Wikaisuksakul, K. Khongkraphan, A. Phon-On, “Convolutional neural networks for recognition of lymphoblast cell images,” Computational Intelligence and Neuroscience, Vol.2019, 2019.
[17] R. Khandekar, P. Shastry, S. Jaishankar, O. Faust, N. Sampathila, “Automated blast cell detection for Acute Lymphoblastic Leukaemia diagnosis,” Biomed. Signal Process.Control, Vol.68, 2021.
[18] Chayan Mondal, Md. Kamrul Hasan, Mohiuddin Ahmad, Md. Abdul Awal, Md. Tasnim Jawad, Aishwariya Dutta, Md. Rabiul Islam, Mohammad Ali Moni, “Ensemble of Convolutional Neural Networks to diagnose Acute Lymphoblastic Leukaemia from microscopic images,” Informatics in Medicine Unlocked, Vol.27, 2021.
[19] Ahmed Abul Hasanaath, Abdul Sami Mohammed, Ghazanfar Latif, Sherif E. Abdelhamid, Jaafar Alghazo, Ahmed Abul Hussain, “Acute lymphoblastic leukaemia detection using ensemble features from multiple deep CNN models,” Electronic Research Archive, Vol.32, Issue.4, pp. 2407-2423, 2024.
[20] Pradeep Kumar Das, Sukadev Meher, “An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukaemia,” Expert Systems with Applications, Vol.183, 2021.
[21] A. Genovese, M. S. Hosseini, V. Piuri, K. N. Plataniotis, F. Scotti, "Acute Lymphoblastic Leukaemia Detection Based on Adaptive Unsharpening and Deep Learning," In the Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Canada, pp.1205-1209, 2021.
[22] Pathan Mohd Shafi, Vijaykumar Bidve, Haribhau Bhapkar, Prashant Dhotre, Veer Bhadra Pratap Singh, “Leukaemia detection system using convolutional neural networks by means of microscopic pictures,” Indonesian Journal of Electrical Engineering and Computer Science, Vol.31, Issue.3, pp.1616-1623, 2023.
[23] Mahnaz M. Kazi, Pina J. Trivedi, Dharmesh M. Patel, Priya K. Varma, Archana Patel, “IGH gene rearrangement in Acute Lymphoid Leukaemia: A study from western region of India,” International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.2, pp.77-82, 2020.
[24] Siddhika Arunachalam, “Applications of Machine Learning and Image Processing Techniques in the Detection of Leukaemia,” International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.2, pp.77-82, 2020.