Advancements in Red Blood Cell Detection using Convolutional Neural Networks

Extraction of data from video sequences of experiments is necessary for the acquisition of high volumes of data. The process requires Red Blood Cell detection to be of sufficient quality, so that the tracking algorithm has enough information for connecting frames and positions together. When holes occur in the detection, the tracking algorithm is only capable of fixing a certain amount of errors before it fails. In this work we iterate on existing frameworks and we attempt to improve upon the existing results of Convolutional Neural Network solutions.