USING FASTER RCNN FOR CELL IMAGE DETECTION

Abstract

There is an ever-growing need for automated detection (and classification) of microscopic images containing cellular samples. To this end, the focus of this work is to provide a method of performing this detection: through the implementation of a Faster RCNN model. The .tiff images for training and testing are fed to the network, with various hyperparameter adjustments between runs, and the anchors/bounding boxes are calculated by FRCNN. Four separate output loss functions are calculated and then unified for a final metric. The network utilized an underlying VGG-16 architecture, and a RPN (Region Proposal Network) which is responsible for the aforementioned bounding boxes. The architecture is run on keras (tensorflow backend).