Transforming Blood Cell Detection and Classification with Advanced Deep Learning Models: A Comparative Study

TL;DR

This study compares advanced deep learning models for blood cell detection and classification, demonstrating that YOLOv10 outperforms others in accuracy and real-time performance, and introduces a new dataset for future research.

Contribution

It provides a comprehensive comparison of deep learning models for blood cell analysis and introduces a new annotated dataset to advance medical diagnostics.

Findings

YOLOv10 outperforms MobileNetV2, ShuffleNetV2, and DarkNet in accuracy and real-time detection.

Increased training epochs improve model performance significantly.

MobileNetV2 and ShuffleNetV2 are more computationally efficient, while DarkNet excels in feature extraction.

Abstract

Efficient detection and classification of blood cells are vital for accurate diagnosis and effective treatment of blood disorders. This study utilizes a YOLOv10 model trained on Roboflow data with images resized to 640x640 pixels across varying epochs. The results show that increased training epochs significantly enhance accuracy, precision, and recall, particularly in real-time blood cell detection & classification. The YOLOv10 model outperforms MobileNetV2, ShuffleNetV2, and DarkNet in real-time performance, though MobileNetV2 and ShuffleNetV2 are more computationally efficient, and DarkNet excels in feature extraction for blood cell classification. This research highlights the potential of integrating deep learning models like YOLOv10, MobileNetV2, ShuffleNetV2, and DarkNet into clinical workflows, promising improvements in diagnostic accuracy and efficiency. Additionally, a new, well-annotated blood cell dataset was created and will be open-sourced to support further advancements in automatic blood cell detection and classification. The findings demonstrate the transformative impact of these models in revolutionizing medical diagnostics and enhancing blood disorder management