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Applying Faster R-CNN for Hematocytes Detection on Compound Microscope with Image Sensor Device and Multiple GPU Computation

In this paper, we demonstrate a machine learning algorithm with multiple GPU processing in hematocytes detection. Object detection in compound microscopy images presents a specific task. Microscopy image import directly to a specimen slide under the compound microscope by an image sensor device. We propose Faster R-CNN with a customizing model applying cutting-edge object detection systems. The MicrosisDCN (Microbes Diagnosis with Deep Convolutional Neural Network) deploys Faster R-CNN in python script configures to access multiple GPUs computation with 7168 CUDA cores of dual GPUs with Linux command options: worker_replicas and num_clones equal to several graphics processing units. The datasets consist of hematocytes extracted from raw slides under a microscope. These images separate the cells of interest into three groups: red blood cell (RBCs), white blood cell (WBCs), and platelets. The training dataset consists of 80 percent of the 40,000 images. And the testing dataset consists of 20 percent of the 40,000 images. Our algorithm also provides the result of mean average precision (mAP) and enables multiple GPU training models in Tensorflow and OpenCV. The mAP is the average of average precision (AP) with intersection-over-union (IoU) in measuring the score of object detection accuracy. If the mAP score approaches 1.0, it indicates well accuracy. Our 9,000 steps valuation algorithm model by the python script estimates the mAP in 3 groups: RBCs, WBCs, and platelets as about 0.9147, 0.9664, and 0.9548. The optimum would be at nearly 12,000 steps because the algorithm estimates the mAP model as 0.96 in all types. The experiment aims to verify the neural network model using a compound microscope.