Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method

Abstract

Background Researchers have tried to identify and count different blood cells in microscopic smear images by using deep learning methods of artificial intelligence to solve the highly time-consuming problem. Results The three types of blood cells are platelets, red blood cells, and white blood cells. This study used the Resnet50 network as a backbone network of the single shot detector (SSD) for automatically identifying and counting different blood cells and, meanwhile, proposed a systematic method to find a better combination of algorithm hyperparameters of the Resnet50 network for promoting accuracy for identifying and counting blood cells. The Resnet50 backbone network of the SSD with its optimized algorithm hyperparameters, which is called the Resnet50-SSD model, was developed to enhance the feature extraction ability for identifying and counting blood cells. Furthermore, the algorithm hyperparameters of Resnet50 backbone networks of the SSD were optimized by the Taguchi experimental method for promoting detection accuracy of the Resnet50-SSD model. The experimental result shows that the detection accuracy of the Resnet50-SSD model with 512 × 512 × 3 input images was better than that of the Resnet50-SSD model with 300 × 300 × 3 input images on the test set of blood cells images. Additionally, the detection accuracy of the Resnet50-SSD model using the combination of algorithm hyperparameters got by the Taguchi method was better than that of the Resnet50-SSD model using the combination of algorithm hyperparameters given by the Matlab example. Conclusion In blood cell images acquired from the BCCD dataset, the proposed Resnet50-SSD model had higher accuracy in identifying and counting blood cells, especially white blood cells and red blood cells.

Automatic Blood Cell Detection Based on Advanced YOLOv5s Network

“…They achieved a mAP of 74.37% with ResNet50. Yao-Mei Chen et al [26] used a single shot detector (SSD) to automatically identify and calculate various BCs. They applied Resnet50 as backbone network and reached a mAP of 77.47%.…”

Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection

“…Works such as [7][8][9] propose approaches based on deep learning, such as U-Net and feature pyramid network (FPN) networks, to perform cell detection and segmentation. A single-shot detector (SSD) in pair with a convolutional neural network (CNN) to localize and count different blood cell types was addressed in [10]. Furthermore, microscopy cell counting based on density estimation employing fully convolutional regression networks was proposed in [11].…”