An Intelligent Approach for Blood Cell Detection Employing Faster RCNN
Abstract
A CBC (complete blood count) is an essential component of a thorough medical evaluation. The practice of medicine has had a significant impact on popular methodologies, such as manual counting and automated analyzers. The shape of blood cells and other cellular characteristics are highly sensitive to contamination levels in the human body. Tiny blood cell images are studied to spot disease and deviations from the norm that may indicate internal contamination. Correct cell segmentation enables more precise and powerful disease detection. Examining blood cells under a microscope is an important part of any pathological investigation. It emphasizes the examination of the correct malady after pinpointing its precise location and then ranking its anomalies, which plays a crucial role in determining the nature of a patient's condition, planning treatment, and evaluating the outcome of that treatment. Initially the complete blood count (CBC) test of patient is carried out if the report suggested that there is abnormality in the blood than that blood is poured on strip and blood is stained out through addition of coagulant. Expert hematologists are in short supply, particularly in underdeveloped nations, and are frequently overwhelmed. To help them with their burden, we provide a unique method for the automated assessment of family disease using artificial intelligence on blood smear images in this paper. For object detection to be useful in real life, it needs to be quick and good at finding different things in an image. There have been many improvements made to object detection, such as Convolutional Neural Network and the family of R-CNN. In our research, we employ Faster R-CNN to look for RBCs, WBCs, and Platelets in the Blood Cell Count (CBC) and Detection dataset. The number, shape, and other meta-information, or any abnormalities in the different parts of blood, could help find problems and/or diseases like leukemia, anemia, lymphoma, sickle cell disease, thrombocytopenia, leukopenia etc., early on. Our model is accurate enough to find a bounding box on the blood parts. A box is drawn along with their name around the various components of the blood smear slide.
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