A Survey of Artificial Intelligence Techniques for Early Leukemia Detection: Advances in Deep Learning, Blood Smear Analysis, and Intelligent Hematology Systems
Keywords:
Blood cell detection, Blood smear image analysis, Computer-Aided Diagnosis (CAD), Convolutional Neural Networks (CNN), Explainable Artificial Intelligence (XAI), Hybrid learning models, Leukemia detectionAbstract
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.
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