Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by the rapid proliferation of immature white blood cells in the bone marrow. Early and accurate diagnosis is essential for improving clinical outcomes; however, distinguishing between lymphocytes and lymphoblasts poses significant challenges owing to their subtle morphological similarities. Traditional manual diagnostic methods, which rely on expert evaluations, are inherently time-consuming and subject to human error. In recent years, machine learning and deep learning approaches have emerged as promising tools for automating and enhancing diagnostic processes. This review systematically examines state-of-the-art traditional and deep learning techniques applied for ALL detection and classification. We provide a comprehensive analysis of various methodologies, including supervised machine learning algorithms and advanced deep learning architectures, with a focus on critical stages such as image preprocessing, feature extraction, and blast cell quantification. Furthermore, we discuss the performance metrics and accuracy benchmarks, highlighting the potential of these techniques to match or exceed human diagnostic capabilities. The review concludes with a discussion of the current challenges, recent developments, and future directions in the application of artificial intelligence for ALL diagnosis, underscoring the need for continued innovation to meet emerging clinical demands. • Provides a comprehensive review of machine learning and deep learning techniques for acute lymphoblastic leukemia (ALL) diagnosis. • Analyzes key stages in the diagnostic pipeline, including image preprocessing, feature extraction, and blast cell classification. • Evaluates model performance across diverse datasets, highlighting the potential of AI methods to rival expert-level accuracy. • Explores recent advancements such as transfer learning, explainable AI (XAI), and vision transformers in hematological imaging. • Discusses major challenges and proposes future directions to improve reliability, scalability, and clinical integration of AI-based ALL diagnosis systems.
Shah et al. (Sun,) studied this question.