Blood Cell Detection and Counting Using Deep Learning
Jasmitha Bhimavarapu, Blessey Kota, D. Rajeswara Rao, Anusha Varla · 2024
Manual blood cell counting in microscopic images is a laborious and error-prone task that poses significant challenges in healthcare diagnostics and hematology research. The traditional method not only consumes valuable time but also introduces the task of human error, potentially leading to inaccurate results and delayed patient care. To address these challenges, this project presents an automated blood cell Identification and Enumeration system built on the YOLO (You Only Look Once) algorithm. This innovative solution aims to revolutionize blood cell analysis by leveraging cutting-edge deep learning technology. By automating the process, it significantly reduces the time required on blood cell counting and minimizes the possibility for human errors. The System detects and classifies various blood cell types, including red blood cells, white blood cells, and platelets, with high accuracy and efficiency. Moreover, it offers a user-friendly interface for healthcare professionals and researchers, facilitating easy input of microscopic images and providing real-time detection results. Preliminary results indicate a detection accuracy of 97.2%, showcasing the system's reliability. This project holds the potential to transform the landscape of hematology, enhancing the speed and precision of blood cell analysis, ultimately improving patient care and advancing scientific understanding in the field of hematology.