Transforming Blood Cell Detection and Classification with Advanced Deep Learning Models: A Comparative Study
This addresses the problem of improving diagnostic accuracy and efficiency for blood disorders in clinical settings, but it is incremental as it applies existing deep learning models to a medical domain.
The study tackled blood cell detection and classification by comparing YOLOv10, MobileNetV2, ShuffleNetV2, and DarkNet models, finding that YOLOv10 outperformed others in real-time performance with increased training epochs enhancing accuracy, precision, and recall.
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