Blood cell detection using YOLOv10
OtherImageObject Detection
Blood cell detection using YOLOv10 involved identifying and classifying white blood cells, red blood cells, and platelets from blood smear images. The main focus was the development of an object detection pipeline to accurately annotate and label the cellular components for downstream analysis. Robust data annotation techniques were employed to ensure cellular features were captured with high precision.
• Labeled thousands of blood smear images for cellular component identification.
• Employed bounding boxes to mark WBCs, RBCs, and platelets.
• Used YOLOv10 pipeline for training and validation.
• Contributed to improved dataset quality for medical image analysis.
Blood cell detection using YOLOv10 involved identifying and classifying white blood cells, red blood cells, and platelets from blood smear images. The main focus was the development of an object detection pipeline to accurately annotate and label the cellular components for downstream analysis. Robust data annotation techniques were employed to ensure cellular features were captured with high precision. • Labeled thousands of blood smear images for cellular component identification. • Employed bounding boxes to mark WBCs, RBCs, and platelets. • Used YOLOv10 pipeline for training and validation. • Contributed to improved dataset quality for medical image analysis.
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