A Survey of Methods and Architectures for Segmentation and Classification of White Blood Cancer Cells in Bone Marrow Microscopic Images Using Deep Kronecker Neural Networks

Abstract

Artificial Intelligence (AI) has significantly advanced medical image analysis, particularly in diagnosing hematological malignancies such as leukemia, where early and accurate detection is essential for improving patient outcomes. Leukemia originates in the bone marrow and is characterized by abnormal proliferation of white blood cells, making microscopic examination a critical diagnostic step. Traditional methods are labor-intensive, subjective, and prone to inter-observer variability. Recent developments in deep learning have enabled automated systems capable of accurately segmenting and classifying cancerous cells from bone marrow images. Advanced models such as Convolutional Neural Networks (CNNs), U-Net, Mask R-CNN, and transformer-based architectures have demonstrated strong performance by capturing complex morphological features of leukemic cells. Emerging approaches like Deep Kronecker Neural Networks further enhance efficiency by reducing computational complexity while maintaining high representational capability. Additionally, techniques such as attention mechanisms, multimodal learning, and automated cytology systems improve detection accuracy and diagnostic efficiency. Despite these advancements, challenges such as limited annotated data, class imbalance, domain variability, and lack of interpretability persist. Future research should focus on explainable AI, multimodal data integration, and real-time clinical deployment to develop robust and scalable diagnostic systems.