https://matjournals.net/engineering/index.php/JoIDACS <p><strong>JoIDACS</strong> is a peer reviewed journal in the discipline of Computer Science published by MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of fundamental research papers on all areas of Intelligent Data Analysis and Computational Statistics. The use of domain knowledge in Data Analysis, Evolutionary Algorithms, Machine Learning, Neural Nets, Fuzzy Logic, Statistical Pattern Recognition, Knowledge Filtering, Post-Processing, and all areas of Data Visualization are some topics covered under this journal title. It also includes Data pre-processing (fusion, editing, transformation, filtering, and sampling), Data Engineering, Database Mining Techniques, Tools, and Applications. JoIDACS promotes methodological studies and applications in Data Science and Computational Statistics.</p> en-US Journal of Intelligent Data Analysis and Computational Statistics (p-ISSN: 3049-3056 e-ISSN: 3048-7080) 3049-3056 https://matjournals.net/engineering/index.php/JoIDACS/article/view/4057 <p>This survey presents a comprehensive review of recent Artificial Intelligence (AI)-based approaches for automated leukemia detection and hematological image analysis. The reviewed studies demonstrate the evolution of automated blood-cell detection and counting using Convolutional Neural Networks (CNNs), followed by hybrid Deep Learning–Machine Learning approaches for leukemia classification, optimized CNN architectures, and portable Raspberry Pi-based diagnostic systems. The survey compares the methodologies, performance, advantages, and limitations of these approaches and identifies important research gaps, including limited datasets, computational complexity, restricted leukemia-subtype coverage, limited clinical validation, and the lack of comprehensive patient-monitoring capabilities. Based on these identified gaps, the survey discusses future directions toward integrated hematology intelligence platforms. The proposed HemaSight AI concept extends the reviewed approaches by considering multiple stages of hematological assessment, including early risk prediction, synthetic visualization, relapse monitoring, and AI-assisted patient communication. Thus, the survey establishes a progression from conventional blood-cell analysis toward intelligent, scalable, and clinically applicable hematology systems.</p> Varun Doddagoudar Shashank Vishwa Madhushree M Copyright (c) 2026 Journal of Intelligent Data Analysis and Computational Statistics (p-ISSN: 3049-3056 e-ISSN: 3048-7080) 2026-08-31 2026-08-31 1 10