A Semiautomatic Sperm Cell Data Annotator for Convolutional Neural Network

Convolutional Neural Network (CNN) is one of deep learning methods which is very powerful for solving image processing and computer vision problems. The powerfulness of this method relies on the number and the quality of the training data. The need for numerous and valid annotated training data is crucial. Manual annotation is still the most popular option for obtaining a valid data annotation. There are some manual annotation systems available. Nevertheless, manually annotating a massive amount of data is remarkably tiresome and need plenty of time. This is the problem that we addressed. In this paper, we propose a system to annotate a CNN training data semi-automatically to accelerate the annotation process. We use an extreme case in which the number of objects to annotate inside a video frame is plentiful (334 – 472 objects). We compare the accuracy of its annotation and the speed of the annotation process to the available annotation systems. The result is that our proposed method reduces the average annotation time up to 48.7%.