Real-Time Classification of Leukocytes Using Deep Learning in Microscopic Imaging

Accurate and automated classification of leukocytes is critical for advancing diagnostic capabilities in hematology, enabling efficient detection and monitoring of various disorders. Conventional manual classification techniques are labor-intensive, prone to human error, and unsuitable for real-time applications. This study presents a deep learning-based framework for the real-time classification of leukocytes in microscopic imaging, leveraging convolutional neural networks (CNNs) optimized for performance and accuracy. The model classifies leukocytes into five major categories: neutrophils, lymphocytes, monocytes, eosinophils, and basophils, achieving an accuracy of 97.3%. To address challenges associated with limited labeled datasets, the study employs data augmentation and transfer learning techniques, enabling robust performance across diverse imaging conditions and staining effects. Additionally, an attention mechanism is integrated into the model to highlight key morphological features, enhancing both interpretability and classification precision. The proposed framework is designed for real-time processing, making it suitable for clinical diagnostics, laboratory automation, and point-of-care testing. This work demonstrates the potential of deep learning in achieving high accuracy and scalability in leukocyte classification, with implications for hematology diagnostics and remote healthcare applications. Future research aims to extend the framework for real-time processing of samples, enabling its use in portable diagnostic devices and remote medical service