Real-Time Classification of Leukocytes Using Deep Learning in Microscopic Imaging
Sagar Bharat Shah
Student, Department of Business Analytics, University of Cincinnati, Cincinnati, USA
Siddharth Agarwal
Student, Department of Computers and Communication, Manipal University, Jaipur, India
Rohan Bagulwar
Data Scientist, Department of Data Science and Analytics, Celebal Technologies
Yash Sant
Student, Software Engineering, Delhi Technological University, Delhi, India
Aditya Dilip
Student, Department of Mechatronics Engineering, SRM Institute of Science and Technology, Chennai, India
Ayush Singh
Student, Department of Data Science & Engineering, Manipal University, Jaipur, India
Pujitha Bobbili
Student, Department of Computer Science, SRM, Chennai, India
Dhanshri Ahir
Student, Department of Computer Science, College of Engineering, Pune, India
ABSTRACT:
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 services, further expanding its utility in automated hematology solutions.
Published in: International Journal of Research in Engineering, Science and Management (Volume 7, Issue 12, December 2024)
Page(s): 128-133
Date of Publication: 29/12/2024
Publisher: IJRESM
DOI: https://doi.org/10.5281/zenodo.14569104
Cite as: Sagar Bharat Shah, Siddharth Agarwal, Rohan Bagulwar, Yash Sant, Aditya Dilip, Ayush Singh, Pujitha Bobbili, Dhanshri Ahir, “Real-Time Classification of Leukocytes Using Deep Learning in Microscopic Imaging,” in International Journal of Research in Engineering, Science and Management, vol. 7, no. 12, pp. 128-133, December 2024.