Suma I.J.Islam, M.D. AshikulTihami, Ahmad NafeesAkter M.2026-10-072026-10-072025-01-01I. J. Suma, M. A. Islam, A. N. Tihami and M. Akter, "Multi-Task Deep Learning Framework for Leukemia Cell Classification and Segmentation: A Joint Learning Approach," 2025 28th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2025, pp. 747-752, doi: 10.1109/ICCIT68739.2025.11491680.97983315786712-s2.0-105041633125https://hdl.handle.net/10361/30494The classification of leukemia and segmentation of cells are tasks that have medical image analysis with the potential to enable early diagnosis and treatment. This paper propose a novel multi-task deep learning framework for leukemia cell classification and segmentation via a shared convolutional neural network backbone. We design a singular architecture that overcomes the inefficiencies of traditional single-task methods by jointly optimizing both tasks. The model proposed provides incredibly good performance with a macro-averaged F1-score of 95.09% for classification across four leukemia classes (Benign, Early, Pre, Pro). It also provides segmentation performance with IoU of 55.12% and Dice score of 67.70% on test data. The model achieves 1.87 GFLOPs indicating its computational effectiveness and high accuracy for both tasks. In tests on a comprehensive leukemia dataset, our multitask method not only saves on computing time but also enhances generalization through shared features making it suitable for real-time clinical use.6 Pagesen-USIntegrated circuitsPixelProtocolsNetwork architectureRadio access networksRegional area networksDigital imagesConvolutional neural networksDeep learningMulti-task learningLeukemia classificationMedical image segmentationDeep learningLeukemia--Diagnosis.Artificial intelligence--Medical applications.Conference Proceeding10.1109/ICCIT68739.2025.11491680