Published March 25, 2026 | Version v1
Journal article Open

XHSA-DCNet: An Explainable Hybrid Swin Transformer and Attention-Guided Dense Convolution Network for Automated Leukemia Detection and Classification

Authors/Creators

  • 1. Gangzhou college of technology and Business, Guangzhou Guangdong, China
  • 2. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

Contributors

  • 1. Guangzhou college of technology and Business, Guangzhou Guangdong, China
  • 2. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

Files

IJCIE_1_1_5.pdf

Files (639.6 kB)

Name Size Download all
md5:3f5abbcd45ac6891066233ec0f6f7d47
639.6 kB Preview Download

Additional details

Identifiers

URL
https://ijcie.com/ijcie/article/view/19

References

  • [1] M. Loey, F. Smarandache, and N. E. M. Khalifa, "Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning," Symmetry, vol. 12, no. 4, pp. 1–20, 2020. [2] A. Ahmed, A. Nagy, A. Kamal, and D. Farghl, "Leukemia detection based on microscopic blood smear images using deep learning," arXiv preprint arXiv:2301.03367, 2022. [3] G. Vieira and M. E. Valle, "Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks," arXiv preprint arXiv:2205.13273, 2022. [4] F. M. Talaat and S. A. Gamel, "Machine learning in detection and classification of leukemia using C-NMC_Leukemia," Multimedia Tools and Applications, vol. 83, pp. 8063–8076, 2024. [5] H. H. R. Rasheed and A. M. Abdulazeez, "Leukemia Detection and Classification Based on Machine Learning and CNN: A Review," Indonesian Journal of Computer Science, vol. 13, no. 3, pp. 1705–1721, 2024. [6] F. Al-Obeidat et al., "Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images: A systematic review and meta-analysis," Frontiers in Big Data, vol. 7, Art. no. 1402926, 2025. [7] S. I. U. Rahman et al., "Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review," Computer Modeling in Engineering & Sciences, vol. 142, no. 2, pp. 1199–1231, 2025. [8] R. F. O. Kizi, T. P. T. Armand, and H.-C. Kim, "A Review of Deep Learning Techniques for Leukemia Cancer Classification Based on Blood Smear Images," Applied Biosciences, vol. 4, no. 1, pp. 9–32, 2025. [9] Aria, M., Javanmard, Z., Pishdad, D., Jannesari, V., Keshvari, M., Arastonejad, M., Safdari, R. and Akbari, M.E., "Towards Diagnostic Intelligent Systems in Leukemia Detection and Classification: A Systematic Review and Meta-analysis," Journal of Evidence-Based Medicine, vol. 18, no. 1, e70005, 2025. [10] Mollick, M. A. A., Rahman, M. M., Asadujjaman, D. M., Tamim, A., Dristi, N. A., & Hossen, M. T.., "Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning," arXiv preprint arXiv:2501.14228, 2025. [11] M. Maruf, M. M. Haque, and B. Paul, "Deep Learning with Self-Attention and Enhanced Preprocessing for Precise Diagnosis of Acute Lymphoblastic Leukemia from Bone Marrow Smears," arXiv preprint arXiv:2508.17216, 2025. [12] U. Ponnusamy and V. Perumal, "Comprehensive review on learning models of leukemia detection based on morphological information," Leukemia & Lymphoma, vol. 67, no. 2, pp. 255–281, 2026. [13] Shah, W.H., Fatima, S.R., Jaimes-Reátegui, R., Arévalo-Simental, D.E., Villalobos-Gutiérrez, P.T. and Pisarchik, A.N., "A systematic review of machine and deep learning techniques for acute lymphoblastic leukemia diagnosis," Artificial Intelligence in Medicine, vol. 176, Art. no. 103393, 2026. [14] Ghaderzadeh, M., Aria, M., Hosseini, A., Asadi, F., Bashash, D. and Abolghasemi, H., "A fast and efficient CNN model for B-ALL diagnosis and subtype classification using peripheral blood smear images," International Journal of Intelligent Systems, vol. 37, no. 8, pp. 5113–5133, 2022. [15] A. Mittal, S. Dhalla, S. Gupta, and A. Gupta, "Automated analysis of blood smear images for leukemia detection: A comprehensive review," ACM Computing Surveys, vol. 54, no. 11, pp. 1–37, 2022.