A comparative assessment of deep object detection models for blood smear analysis

A blood smear is a common type of blood test where blood sample is taken from a patient, smear is made from the sample followed by observation of red blood cells, white blood cells and platelets. A pathologist carefully observes the sample and manually counts the number of RBC, WBC and platelets. This entire process from creating a smear to manually counting each element is tedious and susceptible to human errors. That is why, with the advancement of deep learning, various object detection techniques have become useful for automating the process and mitigating human errors in blood smear analysis. This work presents a comparative assessment of three different object detection models namely Faster R-CNN, EfficientDet D3 and CenterNet Hourglass, and presents their respective inference results. The three models have been compared using the COCO evaluation metrics to identify the best model performance for the given task. It is observed that out of the three models, the Faster R-CNN model performs the best in detecting WBCs and platelets in microscopic blood smear images with an average precision of 99.4%. Critical tasks like medical image processing require accurate predictions to prevent unintended ramifications. Therefore, while slower in terms of inference time, Faster R-CNN is the go-to model where accuracy is the priority. The work is also compared with the existing work in this domain to prove its efficiency.