Harnessing deep learning for blood quality assurance through complete blood cell count detection

Abstract

Accurate evaluation of blood cells plays a pivotal role in assessing immune system functionality and diagnosing various disorders. While complete blood counts (CBCs) can be obtained rapidly using cell counters, manual blood smear inspection remains crucial for patient monitoring and verification of results. However, this manual analysis is labor-intensive, time-consuming, and prone to errors. To overcome these challenges, we propose an automated solution combining advanced image-processing techniques and deep learning algorithms to automatically recognize complete blood cells in blood smear images. This study introduces an enhanced CNN method for automated blood cell identification and categorization, addressing the challenges associated with manual blood smear inspection in patient monitoring and result verification. The proposed solution combines advanced image-processing techniques and deep learning algorithms to automatically recognize complete blood cells in blood smear images. The approach involves preprocessing input images and harnessing the power of deep neural networks to enhance predictions and corrections. A novel method is also introduced, effectively utilizing the interaction between model outcomes and geographical information to improve classification quality. The proposed model achieves impressive metrics through extensive training and validation, including 90.18 % accuracy, 91 % precision, 90 % recall, and 88 % F-score, surpassing conventional Computer-Aided Diagnosis (CAD) systems in clinical laboratories. The utilization of independent creation and evaluation of neural algorithms with the Adam optimizer enhances the robustness and reliability of the proposed method. The results demonstrate the potential of the approach in clinical and diagnostic labs, offering accurate and efficient automated CBC recognition. The study outlines the plan to integrate the model with a blockchain-based blood supply management system. This integration aims to ensure a secure and efficient delivery of high-quality blood during transfusions, enhancing patient safety and care. By relieving hematologists from labor-intensive and time-consuming tasks, the technology presented in this study paves the way for a future where precision, excellence, and optimal patient outcomes converge seamlessly in the field of blood cell identification and categorization.

Keywords

• Canny edge detection
• Convolutional neural network
• Deep learning
• Red blood cell
• White blood cell