LEUKEMIA CLASSIFICATION USING AN OPTIMIZED HYBRID MODEL: CNN, RESNET, DNN, AND XGBOOST IN PERSPECTIVE
DOI:
https://doi.org/10.34218/IJCET_16_03_037Keywords:
Fractional Calculus, DCNN, Leukemia, Federated Learning, Fractional Concept, May-fly AlgorithmAbstract
Leukemia is a fatal cancer affecting individuals of all ages, primarily involving abnormal increases in immature white blood cells that impair bone marrow and blood function. Accurate and timely diagnosis is essential but current manual microscopic analysis is slow, error-prone, and challenged by the similarity between healthy and leukemic cells. This study proposes a novel Federated Learning (FL)-based Deep Learning (DL) framework for leukemia detection, ensuring privacy-preserving training across distributed clients. Input images are first denoised using an Adaptive Median Filter (AMF), followed by Scribble2label-based cell segmentation and image augmentation. Features are then extracted and classified using a Dense Convolutional Network (DenseNet), optimized through a proposed Fractional Mayfly Optimization (FMO) method, which integrates the Mayfly Algorithm and Fractional Calculus. Local model updates are aggregated at the server via RV coefficient-weighted averaging. The proposed FMO-DenseNet achieves high performance with 94.3% accuracy, 5.7% loss, 9.2% MSE, 0.965 TNR, and 0.953 TPR.
References
Zakir Ullah M, Zheng Y, Song J, Aslam S, Xu C, Kiazolu GD, Wang L, An attentionbased convolutional neural network for acute lymphoblastic leukemia classification, Applied Sciences 11:10662, 2021.
Ansari S, Navin AH, Sangar AB, Gharamaleki JV, Danishvar S, A customized efficient deep learning model for the diagnosis of acute leukemia cells based on lymphocyte and monocyte images, Electronics 12:322, 2023.
Ramaneswaran S, Srinivasan K, Vincent PDR, Chang CY, Hybrid inception v3 XGBoost model for acute lymphoblastic leukemia classification, Computational and Mathematical Methods in Medicine 1-10, 2021.
Sampathila N, Chadaga K, Goswami N, Chadaga RP, Pandya M, Prabhu S, Bairy MG, Katta SS, Bhat D, Upadya SP, Customized deep learning classifier for detection of acute lymphoblastic leukemia using blood smear images, In Healthcare 10:1812, 2022.
Baig R, Rehman A, Almuhaimeed A, Alzahrani A, Rauf HT, Detecting malignant leukemia cells using microscopic blood smear images: a deep learning approach, Applied Sciences 12:6317, 2022.
Sulaiman A, Kaur S, Gupta S, Alshahrani H, Reshan MSA, Alyami S, Shaikh A, ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images, Diagnostics 13:2121, 2023.
Mallick PK, Mohapatra SK, Chae GS, Mohanty MN, Convergent learning–based model for leukemia classification from gene expression, Personal and Ubiquitous Computing 27:1103-1110, 2023.
Diaz Resendiz JL, Ponomaryov V, Reyes R, Sadovnychiy S, Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation, Cancers 15:3376, 2023.
Jayasudha S, Comparision of preprocess techniques for brain image using machine learning, Information Technology in Industry 9:653-656, 2021.
Lee H, Jeong WK, Scribble2label: Scribble-supervised cell segmentation via selfgenerating pseudo-labels with consistency, In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru 14-23, 2020.
Khalifa NE, Loey M, Mirjalili S, A comprehensive survey of recent trends in deep learning for digital images augmentation, Artificial Intelligence Review 1-27, 2022.
Bahadure NB, Ray AK, Thethi HP, Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM, International journal of biomedical imaging 2017.
Su R, Liu T, Sun C, Jin Q, Jennane R, Wei L, Fusing convolutional neural network features with hand-crafted features for osteoporosis diagnoses, Neuro computing 385:300- 309, 2020.
Varuna Shree N, Kumar TNR, Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network, Brain informatics 5:23-30, 2018.
Dar JA, Srivastava KK, Mishra A. Lung anomaly detection from respiratory sound database (sound signals). Comput Biol Med. 2023 Sep;164:107311. doi: 10.1016/j.compbiomed.2023.107311. Epub 2023 Aug 2. PMID: 37552916.
Bai Y, Guo L, Jin L, Huang Q, A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition, In 2009 16th IEEE International Conference on Image Processing (ICIP) 3305-3308, 2009.
Fan KC, Hung TY, A novel local pattern descriptor—local vector pattern in high-order derivative space for face recognition, IEEE transactions on image processing 23:2877- 2891, 2014.
Kannan P, Shantha Selva Kumari R, VLSI architecture for LGXP texture for face recognition, Journal of Intelligent & Fuzzy Systems 27:2635-2647, 2014.
Zhong Z, Zheng M, Mai H, Zhao J, Liu X, Cancer image classification based on DenseNet model, In Journal of physics: conference series 1651:012143, 2020.
Zervoudakis K, Tsafarakis S, A mayfly optimization algorithm, Computers & Industrial Engineering 145:106559, 2020.
Bhaladhare PR, Jinwala DC, A clustering approach for the-diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm, Advances in Computer Engineering 2014.
Suhila VM, Kovoor BC, Optimized hybrid approach for topic search using log likelihood and RV coefficient, In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) 338-341, 2017.
Roth HR, Chang K, Singh P, Neumark N, Li W, Gupta V, Gupta S, Qu L, Ihsani A, Bizzo BC, Wen Y, Federated learning for breast density classification: A real-world implementation, In Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning: Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru 181-191, 2020.
Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi NZ, A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images, Irbm 43:290-299, 2022.
Ghosh T, Bashar SK, Alam MS, Wahid K, Fattah SA, A statistical feature based novel method to detect bleeding in wireless capsule endoscopy images, In 2014 International Conference on Informatics, Electronics & Vision (ICIEV) 1-4, 2014.
Srivastava, K., & Pal, A. Removal of A Smoke Region Using Maan Iteration Coupled with Cloud Computing.
Konečný J, McMahan HB, Yu FX, Richtárik P, Suresh AT, Bacon D, Federated learning: Strategies for improving communication efficiency, arXiv preprint arXiv:1610.05492 2016.
Goutam D, Sailaja S, Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier, In 2015 IEEE International Conference on Engineering and Technology (ICETECH) 1-5, 2015.
Boldú L, Merino A, Acevedo A, Molina A, Rodellar J, A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images, Computer Methods and Programs in Biomedicine 202:105999, 2021.
Brereton M, De La Salle B, Ardern J, Hyde K, Burthem J, Do we know why we make errors in morphological diagnosis? An analysis of approach and decision-making in haematological morphology, EBioMedicine 2:1224-1234, 2015.
Lilhore UK, Poongodi M, Kaur A, Simaiya S, Algarni AD, Elmannai H, Vijayakumar V, Tunze GB, Hamdi M, Hybrid model for detection of cervical cancer using causal analysis and machine learning techniques, Computational and Mathematical Methods in Medicine 2022.
Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y, Overfeat: Integrated recognition, localization and detection using convolutional networks, arXiv preprint arXiv:1312.6229 2013.
Dar, J.A., Srivastava, K.K. & Lone, S.A. Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals. SN COMPUT. SCI. 3, 378 (2022). https://doi.org/10.1007/s42979-022-01264-0
N. Dwivedi, K. Srivastava and N. Arya, "Sanskrit word recognition using Prewitt's operator and support vector classification," 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN), Tirunelveli, India, 2013, pp. 265-269, doi: 10.1109/ICE-CCN.2013.6528506.
Pal, A., Srivastava, K.K., Kumar, A. (2013). Parallel Character Reconstruction Expending Compute Unified Device Architecture. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_64
Srivastava, K., & Kumar, A. (2011). A new approach of cloud: Computing infrastructure on demand. Trends in Information Management, 7(2).
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Lila Misra, Dr.Rahul Shrivastava (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
