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A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches

by Priyush Panwar, D.A. Mehta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 23
Year of Publication: 2023
Authors: Priyush Panwar, D.A. Mehta
10.5120/ijca2023922978

Priyush Panwar, D.A. Mehta . A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches. International Journal of Computer Applications. 185, 23 ( Jul 2023), 1-8. DOI=10.5120/ijca2023922978

@article{ 10.5120/ijca2023922978,
author = { Priyush Panwar, D.A. Mehta },
title = { A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 23 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number23/32829-2023922978/ },
doi = { 10.5120/ijca2023922978 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:49.588029+05:30
%A Priyush Panwar
%A D.A. Mehta
%T A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 23
%P 1-8
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Leukemia is a haematological disorder which affects blood and bone marrow. Automated leukemia detection is significant for facilitating timely diagnoses and increasing the chances of successful treatment outcomes. Further, the classification of leukemia is crucial because each type demands a distinct treatment strategy. Deep learning and machine learning techniques are being used by computer aided diagnosis systems for their high precision in identifying leukemia from microscopic blood smear images using image processing techniques and gene microarray data using gene expression analysis techniques. However, automation of this task using image processing techniques is challenging due to the uneven structure and overlapping of cells. The large number of genes in microarray data makes the classification challenging, which is accomplished by applying a variety of feature selection techniques. In this study, recent developments in automated leukemia detection and classification employing image processing and gene expression analysis methods are explored. Furthermore, the paper compares and contrasts various techniques to provide a comprehensive overview that can assist in the continuous refinement of the detection and classification process.

References
  1. M.O Aftab, M Javed Awan, S Khalid, R Javed, and H Shabir. Executing spark bigdl for leukemia detection from microscopic images using transfer learning. In 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), pages 216–220, 2021.
  2. N. Ahmed, A. Yigit, Z. Isik, and A. Alpkocak. Identification of leukemia subtypes from microscopic images using convolutional neural network. Diagnostics, 9(3):104, August 2019.
  3. Q. Ul Ain, S. Akbar, S. A. Hassan, and Z. Naaqvi. Diagnosis of leukemia disease through deep learning using microscopic images. In 2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2), pages 1–6, 2022.
  4. H Almazrua and H Alshamlan. A comprehensive survey of recent hybrid feature selection methods in cancer microarray gene expression data. IEEE Access, 10:71427–71449, 2022.
  5. K K Anilkumar, V.J. Manoj, and Sagi M. Automated detection of b cell and t cell acute lymphoblastic leukaemia using deep learning. IRBM, 43:405–413, 2021.
  6. R.G Bagasjvara, Ika Candradewi, Sri Hartati, and Agus Harjoko. Automated detection and classification techniques of acute leukemia using image processing: A review. pages 35–43, 10 2016.
  7. T Bhadra, S Mallik, A Sohel, and Z Zhao. Unsupervised feature selection using an integrated strategy of hierarchical clustering with singular value decomposition: An integrative biomarker discovery method with application to acute myeloid leukemia. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(3):1354– 1364, may-june 2022.
  8. Z Boreiri, A.N Azad, and A Ghodousian. A convolutional neurofuzzy network using fuzzy image segmentation for acute leukemia classification. In 2022 27th International Computer Conference, Computer Society of Iran (CSICC), pages 1–7, 2022.
  9. Ma´ıla Claro, Luis Vogado, Rodrigo Veras, Andr´e Santana, Jo˜ao Tavares, Justino Santos, and Vinicius Machado. Convolution neural network models for acute leukemia diagnosis. In 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pages 63–68, 2020.
  10. P. K. Das, D. V. A, S. Meher, R. Panda, and A. Abraham. A systematic review on recent advancements in deep and machine learning based detection and classification of acute lymphoblastic leukemia. IEEE Access, 10:81741–81763, 2022.
  11. P.K Das, B Sahoo, and S Meher. An efficient detection and classification of acute leukemia using transfer learning and orthogonal softmax layer-based model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022.
  12. Pradeep Kumar Das, Priyanka Jadoun, and Sukadev Meher. Detection and classification of acute lymphocytic leukemia. In 2020 IEEE-HYDCON, pages 1–5, 2020.
  13. A.K. Dwivedi. Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput Applic, 29:1545–1554, 2018.
  14. B.C et al Feltes. Cumida: An extensively curated microarray database for benchmarking and testing of machine learning approaches in cancer research. Journal of Computational Biology, 2019.
  15. R Francese,MFrasca,MRisi, and G Tortora. A deep learning and genetic algorithm based feature selection processes on leukemia data. In 2022 26th International Conference Information Visualisation (IV), pages 412–417, 2022.
  16. E. U. Francis, M. Y. Mashor, R. Hassan, and A. A. Abdullah. Screening of bone marrow slide images for leukemia using multilayer perceptron (mlp). In Proc. IEEE Symp. Ind. Electron. Appl., pages 643–648, September 2011.
  17. A. Genovese, M. S. Hosseini, V. Piuri, K. N. Plataniotis, and F. Scotti. Acute lymphoblastic leukemia detection based on adaptive unsharpening and deep learning. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1205–1209, Toronto, ON, Canada, 2021.
  18. T.R et al Golub. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring”. Science, pages 531–537, 1999.
  19. A. Gupta and R. Gupta. All challenge dataset of isbi 2019. The Cancer Imaging Archive, 2019.
  20. A. Gupta and R. Gupta. Sn-am dataset: White blood cancer dataset of b-all and mm for stain normalization. The Cancer Imaging Archive, 2019.
  21. American Society Of Haemotology. Ash-dataset. http:// imagebank.hematology.org/.
  22. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  23. S.H Kassani, P.H Kassani, M.J Wesolowski, K.A Schneider, and R Deters. A hybrid deep learning architecture for leukemic blymphoblast classification. In 2019 International Conference on Information and Communication Technology Convergence (ICTC), pages 271–276, 2019.
  24. N. Koul and S. S. Manvi. Ensemble feature selection from cancer gene expression data using mutual information and recursive feature elimination. In 2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC), pages 1–6, Bengaluru, India, 2020.
  25. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6):84–90, 2017.
  26. Deepika Kumar, Nikita Jain, Aayush Khurana, Sweta Mittal, Suresh Chandra Satapathy, Roman Senkerik, and Jude D. Hemanth. Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks. IEEE Access, 8:142521–142531, 2020.
  27. P Kumar and S.M Udwadia. Automatic detection of acute myeloid leukemia from microscopic blood smear image. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 1803– 1807, 2017.
  28. R. D. Labati, V. Piuri, and F. Scotti. All-idb: The acute lymphoblastic leukemia image database for image processing. In Proc. 18th IEEE Int. Conf. Image Process., pages 2045– 2048, September 2011.
  29. A.S Abdul Nasir, N Mustafa, and N.M Nasir. Application of thresholding technique in determining ratio of blood cells for leukemia detection. In Proc Int Conf Man-Mach Syst (ICoMMS), pages 1–6, Penang, Malaysia, October 2009.
  30. A.S Negm, O.A Hassan, and A.H Kandil. A decision support system for acute leukaemia classification based on digital microscopic images. Alexandria Eng J, 57(4):2319–2332, December 2018.
  31. N Patel and A Mishra. Automated leukaemia detection using microscopic images. Proc Comput Sci, 58:635–642, January 2015.
  32. S Patel, H Patel, D Vyas, and S Degadwala. Multi-classifier analysis of leukemia gene expression from curated microarray database (cumida). In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), pages 1174–1178, 2021.
  33. S. Rajeswari, Ch. Siva Vasanth, Ch. Bhavana, and K Sethu Sandeep Chowdary. Detection and classification of various types of leukemia using image processing, transfer learning and ensemble averaging techniques. In 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), pages 1–6, 2022.
  34. A. Rehman, N. Abbas, T. Saba, S. I. U. Rahman, Z. Mehmood, and H. Kolivand. Classification of acute lymphoblastic leukemia using deep learning. Microsc. Res. Technique, 81(11):1310–1317, November 2018.
  35. S. Shafique and S. Tehsin. Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks. Technol. Cancer Res. Treatment, 17:1533033818802789, September 2018.
  36. S. Shafique, S. Tehsin, S. Anas, and F. Masud. Computerassisted acute lymphoblastic leukemia detection and diagnosis. In 2019 2nd International Conference on Communication, Computing and Digital systems (C-CODE), pages 184–189, 2019.
  37. A. Shah, S. S. Naqvi, K. Naveed, N. Salem, M. A. U. Khan, and K. S. Alimgeer. Automated diagnosis of leukemia: A comprehensive review. IEEE Access, 9:132097–132124, 2021.
  38. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition, 2014.
  39. Badem H. Okumus I.T Simsek, E. Leukemia sub-type classification by using machine learning techniques on gene expression. In Proceedings of Sixth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems, vol 217. Springer, Singapore, 2022.
  40. E. Suryani, S. Palgunadi, and T. N. Pradana. Classification of acute myelogenous leukemia (aml m2 and aml m3) using momentum back propagation from watershed distance transform segmented images. J. Phys., Conf. Ser., 801:012044, January 2017.
Index Terms

Computer Science
Information Sciences

Keywords

Leukemia Image Segmentation Convolutional Neural Networks Feature Selection

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