Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors
DOI:
https://doi.org/10.21928/uhdjst.v6n1y2022.pp21-33Keywords:
Leukemia Diagnosis, Blood Smear, Feature Extraction, Machine LearningAbstract
Leukemia is characterized by an abnormal proliferation of leukocytes in the bone marrow and blood, which is usually detected by pathologists using a microscope to examine a blood smear. Leukemia identification and diagnosis in advance are a trending topic in medical applications for decreasing the death toll of individuals with Acute Lymphoblastic Leukemia (ALL). It is critical to analyze the white blood cells for the identification of ALL for which the blood smear images are utilized. This paper discusses and presents a micro-pattern descriptor, called Local Directional Number Pattern along with Multi-scale Weber Local Descriptor for feature extraction task to determine cancerous and noncancerous blood cells. A balanced dataset with 260 blood smear images from the ALL-IDB2 dataset was used as training data. Consequently, a proposed model was constructed by applying different individual and combined feature extraction methods, and fed into the machine learning classifiers (Decision Tree, Ensemble, K-Nearest Neighbors, Naïve Bayes, and Random Forest) to determine cancerous and noncancerous blood cells. Experimental results indicate that the developed feature fusion technique assured a reasonable performance compared to other existing works with a testing average accuracy of 97.69 ± 1.83% using Ensemble classifier.
References
C. Di Ruberto, A. Loddo and G. Puglisi G. “Blob detection and deep learning for leukemic blood image analysis”. Applied Sciences, vol. 10, no. 3, p. 1176, 2020.
G. Drałus, D. Mazur and A. Czmil. “Automatic detection and counting of blood cells in smear images using retinanet”. Entropy, vol. 23, no. 11, p. 1522, 2021.
B. George-Gay and K. Parker. “Understanding the complete blood count with differential”. Journal of PeriAnesthesia Nursing, vol. 18, pp. 96-117, 2003.
S. Shafique and S. Tehsin. “Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks”. Technology in Cancer Research and Treatment, vol. 17, pp. 1-7, 2018.
D. A. Arber, A. Orazzi, R. Hasserjian, J. Thiele, M. J. Borowitz, M. Le Beau, C. D. Bloomfield, M. Cazzola and J. W. Vardiman. “The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia”. Blood, vol. 127, pp. 2391-2405, 2016.
F. Huang, P. Guang, F. Li, X. Liu, W. Zhang and W. Huang. “AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: A STARD compliant diagnosis research”. Medicine (Baltimore), vol. 99, p. e23154, 2020.
Y. Dong, O. Shi, Q. Zeng, X. Lu, W. Wang, Y. Li and Q. Wang. “Leukemia incidence trends at the global, regional, and national level between 1990 and 2017. Experimental Hematology and Oncology, vol. 9, pp. 1-11, 2020.
M. Ghaderzadeh, F. Asadi, A. Hosseini, D. Bashash, H. Abolghasemi and A. Roshanpour. “Machine learning in detection and classification of leukemia using smear blood images: A systematic review”. Scientific Programming, vol. 2021, p. 9933481, 2021.
M. Kim, K. Chae, S. Lee, H. J. Jang and S. Kim. “Automated classification of online sources for infectious disease occurrences using machine-learning-based natural language processing approaches”. International Journal of Environmental Research and Public Health, vol. 17, pp. 1-13, 2020.
F. E. Al-Tahhan, M. E. Fares, A. A. Sakr and D. A. Aladle. “Accurate automatic detection of acute lymphatic leukemia using a refined simple classification”. Microscopy Research and Technique, vol. 83, pp. 1178-1189, 2020.
M. Sharif, J. Amin, A. Siddiqa, H. U. Khan, M. S. A. Malik, M. A. Anjum and S. Kadry. “Recognition of different types of leukocytes using YOLoV2 and optimized bag-of-features”. IEEE Access, vol. 8, pp. 167448-167459, 2020.
K. K. Jha and H. S. Dutta. “Nucleus and cytoplasm–based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images”. Medical and Biological Engineering and Computing, vol. 58, pp. 171-186, 2020.
L. H. S. Vogado, R. M. S. Veras, F. H. D. Araujo, R. R. V. Silva and K. R. T. Aires. “Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification”. Engineering Applications of Artificial Intelligence, vol. 72, pp. 415-422, 2018.
K. Muthumayil, S. Manikandan, S. Srinivasan, J. Escorcia- Gutierrez, M. Gamarra and R. F. Mansour. “Diagnosis of leukemia disease based on enhanced virtual neural network”. Computers, Materials and Continua, vol. 69, pp. 2031-2044, 2021.
V. Singhal and P. Singh. “Texture Features for the Detection of Acute Lymphoblastic Leukemia.” 2016.
S. Praveena and S. P. Singh. “Sparse-FCM and deep convolutional neural network for the segmentation and classification of acute lymphoblastic leukaemia”. Biomed Technologies, vol. 65, pp. 759- 773, 2020.
D. Umamaheswari and S. Geetha. “A framework for efficient recognition and classification of acute lymphoblastic leukemia with a novel customized-KNN classifier”. The Journal of Computer Engineering and Information Technology, vol. 26, pp. 131-140, 2018.
M. Tuba and E. Tuba. “Generative adversarial optimization (GOA) for acute lymphocytic leukemia detection”. Studies in Informatics and Control, vol. 28, pp. 245-254, 2019.
S. Hariprasath, T. Dharani, S. Mohammad and N. Bilal. “Automated Detection of Acute Lymphocytic Leukemia Using Blast Cell Morphological Features”. 2nd International Conference on Advances in Science and Technology (ICAST) 2019 on 8th, 9th April 2019 by K J Somaiya Institute of Engineering and Information Technology, Mumbai, India, 2019.
A. Bodzas, P. Kodytek and J. Zidek. “Automated detection of acute lymphoblastic leukemia from microscopic images based on human visual perception”. Frontiers in Bioengineering and Biotechnology, vol. 8, pp. 1-13, 2020.
K. N. Sukhia, M. M. Riaz, A. Ghafoor and N. Iltaf. “Overlapping white blood cells detection based on watershed transform and circle fitting”. Radioengineering, vol. 26, pp. 1177-1181, 2017.
Conference II, Processing I. ALL-IDB : The Acute Lymphoblastic Leukemia Image Database for Image Processing Ruggero Donida Labati. Vincenzo Piuri, Fabio Scotti Università degli Studi di Milano, Department of Information Technology, IEEE International Conference on Image Processing, pp. 2089-2092, 2011.
F. H. Ahmad and S. H. Wady. “COVID 19 infection detection from chest X ray images using feature fusion and machine learning”. Scientific Journal, Vol. 5, pp. 10-30, 2021.
A. R. Rivera, J. R. Castillo and O. Chae. “Local directional texture pattern image descriptor”. Pattern Recognition Letters, vol. 51, pp. 94-100, 2015.
R. R. Rose, A. Suruliandi and K. Meena. “Local texture description framework-based modified local directional number pattern: A new descriptor for face recognition”. International Journal of Biometeorology, vol. 7, pp. 147-169, 2015.
S. P. Ramalingam. “Paturu Venkata Subbu Sita Rama CM. Dimensionality reduced local directional number pattern for face recognition”. Journal of Ambient Intelligence and Humanized Computing, vol. 9, pp. 95-103, 2018.
R. A. Kirsch. “Computer determination of the constituent structure of biological images”. Computers and Biomedical Research, vol. 4, pp. 315-328, 1971.
A. El Idrissi, Y. El Merabet and Y. Ruichek. “Palmprint recognition using state-of-the-art local texture descriptors: A comparative study”. IET Biometrics, vol. 9, pp. 143-153, 2020.
Sachinkumar and S. Raga. “Breast cancer detection technique based on multi-subspace randomization and collaboration”. International Journal of Advanced Research in Engineering and Technology, vol. 11, pp. 687-701, 2020.
A. Banerjee, N. Das and K. C. Santosh. “Weber local descriptor for image analysis and recognition: A survey”. The Visual Computer, vol. 38, pp. 321-243, 2020.
M. Hussain, S. Qasem, G. Bebis, G. Muhammad, H. Aboalsamh and H. Mathkour. “Evaluation of image forgery detection using multi-scale weber local descriptors”. International Journal on Artificial Intelligence Tools, vol. 24, pp. 416-424, 2015.
S. Ahmed. “Ethnicity identification based on fusion strategy of local and global features extraction”. International Journal of Multidisciplinary and Current Research, vol. 4, pp. 200-205, 2016.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Shakhawan Hares Wady
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
