Abstract
A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.
Highlights
A complete blood count (CBC) is a typical clinical test that provides relevant information for disease diagnosis
We proposed an approach that employs RetinaNet based on convolutional neural network (CNN) architecture to detect all three types of blood cells, i.e., Red Blood Cells (RBCs), White Blood Cells (WBCs), and platelets simultaneously
The tests of the developed models were performed for 15 images with RBCs, 151 images with WBCs, and 64 images with platelets
Summary
A complete blood count (CBC) is a typical clinical test that provides relevant information for disease diagnosis. CBC provides information about the production of all blood cells, identifies the patient’s ability to carry oxygen by evaluating RBC counts, and allows for immune system evaluation by assessing WBC counts with differential. This test helps diagnose anemia, certain cancers, infections, and many many others, as well as monitor the side effects of certain medications [1]. For this reason, medical laboratories are flooded with a large number of blood and tissue samples that need to be analyzed as accurately as possible and in the shortest possible time. The ability to accurately quantitate specific populations of cells is important for precision diagnostics in laboratory medicine
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