2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 224-252, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Abirami M. 1 * , Victo Sudha George G. 2 , Dahlia Sam S. 3
Doi: https://doi.org/10.54216/FPA.190217
Leukemia is a very dangerous kind of malignancy troubling the blood or bone marrow in all age categories, both in adults and children. The deadly and threatening kind of leukemia is named Acute Lymphoblastic Leukemia (ALL). The accurate and automated ALL diagnosis of blood cancer is complex work. Medical experts and hematologists in the bone marrow and blood samples detect it by employing a high-quality microscope. The manual classification is observed as tiresome and is restricted by varying expert considerations and other attributes. Presently, the Convolutional Neural Networks (CNNs) have become an acceptable mechanism for analyzing the medical image. However, for attaining outstanding performance, conventional CNNs normally demand large data sources for better training. Thus, to alleviate the existing complexities, we implemented an effective ALL detection system using deep learning. At first, the necessitated images are aggregated from global resources of data. Further, the garnered images are inputted into the Optimized Trans-Res-Unet+ (OTRUnet+)-based segmentation model. Here, the Fitness-aided Position Updating in the Social engineering Algorithm (FPUSA) for improving the segmentation process’s efficacy optimally tunes the OTRUnet+ technique parameters. In addition, the segmented images are taken to perform the classification process using the Adaptive Multi-Dilated Residual Attention Network (AMDRAN); here several parameters are optimally tuned by the same FPUSA to enrich the classification process. Finally, the suggested AMDRAN technique offered the ALL classified output. The effectiveness of the designed ALL detection system is explored with several existing systems to display its enhanced performance over other models
Acute Lymphoblastic Leukemia , Segmentation and Classification , Optimized Trans-Res-Unet+ , Adaptive Multi-Dilated Residual Attention Network , , Fitness-aided Position Updating in Social engineering Algorithm
[1] J. L. Diaz Resendiz, V. Ponomaryov, R. Reyes Reyes, and S. Sadovnychiy, "Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation," Cancers, vol. 15, no. 3376, 2023.
[2] S. Alagu, A. P. N, and B. B. K, "Automatic detection of acute lymphoblastic leukemia using UNET based segmentation and statistical analysis of fused deep features," Appl. Artif. Intell., vol. 35, pp. 1952–1969, 2021.
[3] S. Anwar and A. Alam, "A convolutional neural network–based learning approach to acute lymphoblastic leukemia detection with automated feature extraction," Med. Biol. Eng. Comput., vol. 58, pp. 3113–3121, 2020.
[4] N. Sampathila et al., "Customized deep learning classifier for detection of acute lymphoblastic leukemia using blood smear images," Healthcare, vol. 10, no. 10, 2022.
[5] U. Saeed et al., "DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification," Multimed. Tools Appl., vol. 83, pp. 21019–21043, 2024.
[6] M. Jawahar, H. Sharen, and A. H. Gandomi, "ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification," Comput. Biol. Med., vol. 148, no. 105894, 2022.
[7] M. Zakir Ullah et al., "An attention-based convolutional neural network for acute lymphoblastic leukemia classification," Appl. Sci., vol. 11, no. 10662, 2021.
[8] L. F. Rodrigues, A. R. Backes, B. A. N. Travençolo, and G. M. B. de Oliveira, "Optimizing a deep residual neural network with genetic algorithm for acute lymphoblastic leukemia classification," J. Digit. Imaging, vol. 35, pp. 623–637, 2022.
[9] E. Özbay, F. A. Özbay, and F. S. Gharehchopogh, "Peripheral blood smear images classification for acute lymphoblastic leukemia diagnosis with an improved convolutional neural network," J. Bionic Eng., pp. 1–17, 2023.
[10] P. K. Das and S. Meher, "An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia," Expert Syst. Appl., vol. 183, no. 115311, 2021.
[11] S. I. Ur Rahman et al., "Efficient segmentation of lymphoblast in acute lymphocytic leukemia," Sci. Program., pp. 1–7, 2021.
[12] A. Abhishek, R. K. Jha, R. Sinha, and K. Jha, "Automated classification of acute leukemia on a heterogeneous dataset using machine learning and deep learning techniques," Biomed. Signal Process. Control, vol. 72, no. 103341, 2022.
[13] C. Mondal et al., "Acute lymphoblastic leukemia detection from microscopic images using weighted ensemble of convolutional neural networks," arXiv preprint, 2021.
[14] G. Atteia, A. A. Alhussan, and N. A. Samee, "BO-ALLCNN: Bayesian-based optimized CNN for acute lymphoblastic leukemia detection in microscopic blood smear images," Sensors, vol. 22, no. 5520, 2022.
[15] S. Ramaneswaran, K. Srinivasan, P. D. R. Vincent, and C. Y. Chang, "Hybrid inception v3 XGBoost model for acute lymphoblastic leukemia classification," Comput. Math. Methods Med., pp. 1–10, 2021.
[16] A. M. Fathollahi-Fard, M. Hajiaghaei-Keshteli, and R. Tavakkoli-Moghaddam, "The Social Engineering Optimizer (SEO)," Eng. Appl. Artif. Intell., vol. 72, pp. 267–293, Jun. 2018.
[17] M. M. Qanbar and S. Tasdemir, "Detection of malaria diseases with residual attention network," Int. J. Intell. Syst. Appl. Eng., vol. 7, pp. 238–244, 2019.
[18] A. V. Panteleev and A. A. Kolessa, "Application of the tomtit flock metaheuristic optimization algorithm to the optimal discrete-time deterministic dynamical control problem," Algorithms, vol. 15, no. 301, 2022.
[19] H. A. Akkar and S. A. Salman, "Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems," Int. J. Intell. Eng. Syst., vol. 13, 2020.
[20] E. Trojovská, M. Dehghani, and P. Trojovský, "Zebra optimization algorithm: A new bio-inspired optimization algorithm for solving optimization algorithm," IEEE Access, vol. 10, pp. 49445–49473, 2022.
[21] V. D. A. Kumar et al., "An effective method for predicting postpartum haemorrhage using deep learning techniques," Multimed. Tools Appl., 2022. Available: https://doi.org/10.1007/s11042-021-11622-4.
[22] E. H. Houssein et al., "Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification," Front. Oncol., vol. 13, no. 1230434, 2023.
[23] N. Takahashi and Y. Mitsufuji, "Densely connected multi-dilated convolutional networks for dense prediction tasks," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp. 993–1002, 2021.
[24] A. H. Alharbi et al., "Segmentation and classification of white blood cells using the UNet," Contrast Media Mol. Imaging, 2022.
[25] Y. Xu, S. Hou, X. Wang, D. Li, and L. Lu, "A medical image segmentation method based on improved UNet 3+ network," Diagnostics, vol. 13, no. 576, 2023.
[26] J. Chen et al., "Transunet: Transformers make strong encoders for medical image segmentation," arXiv preprint, 2021.
