Artificial Neural Network in Classification of Human Blood Cells Using Faster R-CNN
Ike Verawati, Ivan Daniel Parlindungan Hasibuan · 2021
Blood cells are an element found in the human body that has a function in the body's working mechanism. In the medical world, blood can be used as a source of diagnosis of a source of disease, this is because there is a lot of important information contained by blood cells. In conducting the analysis of blood cells, it includes a series of laboratory tests, one of which is a test to determine the morphology of the blood cells. In laboratory tests carried out by doctors and medical personnel, it is usually still done manually, which has a low level of accuracy and precision. This can be caused by the knowledge, physical condition, and also the concentration of the doctor and also the laboratory staff, which makes it possible to get different analysis results. In manual laboratory tests, this can be overcome by creating an automated system that can classify human blood cells using artificial intelligence. In this research, the Faster R-CNN algorithm used 364 images of human blood cells. From the experiment above, the data is divided into 328 training data and also 36 testing data, it can be seen that Faster R-CNN has an accuracy of 94.92% in classifying human red blood cells and white blood cells.