Multi-modal Feature Fusion with Machine Learning Approach for Leukemia Detection and Classification


  • I. Vinurajan, K. P. Sanal Kumar, S. Anu H. Nair


: Feature Fusion; Leukemia Detection; Support Vector Machine; Machine Learning; Contrast Enhancement


Leukemia is a significant cause of death worldwide and is a fatality group of cancer-related disorder that affects all age groups, mainly children and grownups. Mostly, it is related to White Blood Cells (WBC), which is supplemented by an increase in the diverse range of immature lymphocytes and affect negatively the bone marrow or bloodstream. As a result, a reliable and rapid cancer analysis is a fundamental prerequisite for effective treatment to increase the survival rate. At present, a manual diagnosis of blood samples attained by microscopic imageries is done to detect diseases that are time-consuming, less accurate, and often very slow. Moreover, the shape and appearance of leukemic cells look like ordinary cells which make detection challenging, in microscopic analysis. Currently, machine learning (ML) methods have become a better method for medical image analysis. This study presents a Multi-modal Feature Fusion with Machine Learning for Leukemia Detection and Classification (MMFFML-LDC) technique. The MMFFML-LDC technique mostly proposes to identify and categorize the occurrence of leukemia on microscopic blood images. In the MMFFML-LDC system, an initial phase of pre-processing is involved in two levels: median filtering (MF) based noise removal and adaptive histogram equalization (AHE) based contrast enhancement. Furthermore, watershed segmentation is employed to segment the pre-processed images. For feature extraction, a fusion of four ML feature extractors namely histogram of gradients (HOG), local binary pattern (LBP), scale-invariant feature transform (SIFT), and gray level co-occurrence matrix (GLCM). Finally, the detection of leukemia can be performed by the usage of a support vector machine (SVM). The performance analysis of the MMFFML-LDC technique can be studied using a blood image dataset from Kaggle. The experimental values are definite that the MMFFML-LDC system obtains better performance over other ML classifiers.


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Ramagiri, A., Jahnavi, V., Gottipati, S., Monica, C., Afrin, S., Jyothi, B. and Chinnaiyan, R., 2023, March. Image Classification for Optimized Prediction of Leukemia Cancer Cells using Machine Learning and Deep Learning Techniques. In 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA) (pp. 193-197). IEEE.

Arivuselvam, B. and Sudha, S., 2022. Leukemia classification using the deep learning method of CNN. Journal of X-ray science and technology, 30(3), pp.567-585.

Abhishek, A., Jha, R.K., Sinha, R. and Jha, K., 2023. Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by Grad-CAM visualization. Biomedical Signal Processing and Control, 83, p.104722.

Abhishek, A., Jha, R.K., Sinha, R. and Jha, K., 2022. Automated classification of acute leukemia on a heterogeneous dataset using machine learning and deep learning techniques. Biomedical Signal Processing and Control, 72, p.103341.

Gondal, C.H.A., Irfan, M., Shafique, S., Bashir, M.S., Ahmed, M., Alshehri, O.M., Almasoudi, H.H., Alqhtani, S.M., Jalal, M.M., Altayar, M.A. and Alsharif, K.F., 2023. Automated Leukemia Screening and Sub-types Classification Using Deep Learning. Computer Systems Science & Engineering, 46(3).

Hagar, M., Elsheref, F.K. and Kamal, S.R., 2023. A New Model for Blood Cancer Classification Based on Deep Learning Techniques. International Journal of Advanced Computer Science and Applications, 14(6).

Mallick, P.K., Mohapatra, S.K., Chae, G.S. and Mohanty, M.N., 2023. Convergent learning–based model for leukemia classification from gene expression. Personal and Ubiquitous Computing, 27(3), pp.1103-1110.

Das, P.K. and Meher, S., 2021. An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia. Expert Systems with Applications, 183, p.115311.

Bukhari, M., Yasmin, S., Sammad, S., El-Latif, A. and Ahmed, A., 2022. A deep learning framework for leukemia cancer detection in microscopic blood samples using squeeze and excitation learning. Mathematical Problems in Engineering, 2022.

Arivuselvam, B. and Sudha, S., 2022. Leukemia classification using the deep learning method of CNN. Journal of X-ray science and technology, 30(3), pp.567-585.

Sulaiman, A., Kaur, S., Gupta, S., Alshahrani, H., Reshan, M.S.A., Alyami, S. and Shaikh, A., 2023. ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images. Diagnostics, 13(12), p.2121.

Saikia, R., Sarma, A. and Shuleenda Devi, S., 2024. Optimized Support Vector Machine Using Whale Optimization Algorithm for Acute Lymphoblastic Leukemia Detection from Microscopic Blood Smear Images. SN Computer Science, 5(5), p.439.

Sallam, N.M., Saleh, A.I., Arafat Ali, H. and Abdelsalam, M.M., 2022. An efficient strategy for blood diseases detection based on grey wolf optimization as feature selection and machine learning techniques. Applied Sciences, 12(21), p.10760.

Elrefaie, R.M., Mohamed, M.A., Marzouk, E.A. and Ata, M.M., 2024. A robust classification of acute lymphocytic leukemia‐based microscopic images with supervised Hilbert‐Huang transform. Microscopy Research and Technique, 87(2), pp.191-204.

Ahmad, R., Awais, M., Kausar, N., Tariq, U., Cha, J.H. and Balili, J., 2023. Leukocytes classification for leukemia detection using quantum inspired deep feature selection. Cancers, 15(9), p.2507.

Mohan, A., Beshir, K. and Kebede, A., 2024. Acute myelogenous leukaemia detection in blood microscope images using particle swarm optimisation. International Journal of Computational Vision and Robotics, 14(3), pp.250-263.

Batool, A. and Byun, Y.C., 2023. Lightweight EfficientNetB3 model based on depthwise separable convolutions for enhancing classification of leukemia white blood cell images. IEEE Access.

Dinç, İ., Dinç, S., Sigdel, M., Sigdel, M.S., Aygün, R.S. and Pusey, M.L., 2015. DT-Binarize: A decision tree based binarization for protein crystal images. In Emerging trends in image processing, computer vision and pattern recognition (pp. 183-199). Morgan Kaufmann.

Jamil, M.M.A., Oussama, L., Hafizah, W.M., Abd Wahab, M.H. and Johan, M.F., 2019. Computational automated system for red blood cell detection and segmentation. In Intelligent Data Analysis for Biomedical Applications (pp. 173-189). Academic Press.

Kobayashi, T., Hidaka, A. and Kurita, T., 2008. Selection of histograms of oriented gradients features for pedestrian detection. In Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II 14 (pp. 598-607). Springer Berlin Heidelberg.

Rahim, M.A., Hossain, M.N., Wahid, T. and Azam, M.S., 2013. Face recognition using local binary patterns (LBP). Global Journal of Computer Science and Technology, 13(4), pp.1-8.

Kasiselvanathan, M., Sangeetha, V. and Kalaiselvi, A., 2020. Palm pattern recognition using scale invariant feature transform. International Journal of Intelligence and Sustainable Computing, 1(1), pp.44-52.

Garg, M., Malhotra, M. and Singh, H., 2021. A novel machine-learning framework-based on LBP and GLCM approaches for CBIR system. Int. Arab J. Inf. Technol., 18(3), pp.297-305.

Harirchian, E., Lahmer, T., Kumari, V. and Jadhav, K., 2020. Application of support vector machine modeling for the rapid seismic hazard safety evaluation of existing buildings. Energies, 13(13), p.3340.




How to Cite

I. Vinurajan,. (2024). Multi-modal Feature Fusion with Machine Learning Approach for Leukemia Detection and Classification. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3174–3185. Retrieved from



Research Article