Automatic Recognition of Blood Cell Images with Dense Distributions Based on a Faster Region-Based Convolutional Neural Network  
[PERSON]  
1 School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an710048, China; [EMAIL_ADDRESS] (Y.L.); [PERSON]L_ADDRESS].com (M.C.); [EMAIL_ADDRESS] (H.X.); qiangiao  
(SVM) [11] and neural networks [12]. They are used to classify the obtained features and further perform border regression by the target category. However, the traditional detection methods above need to select a suitable model for the automatic recognition of samples with different features; therefore, they are poor in generalization and robustness.  
As deep learning is increasingly integrated into the medical field, much research on cell detection methods of deep learning has been carried out. The typical target detection network is composed of a faster region-based convolutional neural network (Faster R-CNN) [13], you only look once (YOLO) [14] and a single shot multi-box detector (SSD) [15], which mainly focus on cell segmentation or single-cell recognition. In 2018, [PERSON] et al. [16] proposed a method based on a generative adversarial network to realize the detection of cancer cells in pathological sections of breast cancer, in which the U-Net network was a generative network. In 2020, [PERSON] et al. [17] used the K-means algorithm and a CNN to classify and locate white blood cells, respectively, thus separating them from whole blood smear images. In the same year, [PERSON] et al. [18], by referring to a LeNet-5 network, constructed a model using a traditional texture feature classifier and shallow CNN to effectively identify the type of milk somatic cells, which provided a new idea for subsequent research on classification methods. In 2021, [PERSON] et al. [19] combined the xResNet architecture of a CNN with transfer learning to realize cell counting, in which the cell counting problem was transformed into a regression task. In 2022, based on the design criteria of optimizing the accuracy and speed of detection with minimal resources, [PERSON] et al. [20] developed a customized deep-learning architecture (YOLO-mp), and obtained the number of pathogens in microscope images of thick blood smears. Although these methods realize cell recognition and classification, most of them complete single-cell recognition under the premise of sparse distribution. However, dense cell distribution is inevitable in the actual medicine scenario, in which the target sizes caused by adhesion and overlap are different and the cell morphological changes caused by squeezing are irregular. There, the problem of unrecognizable adherent cells appears, resulting in an increased false detection rate. The current algorithm research on these cases is usually limited to general target detection or specific scene detection. Few studies involve the extraction of adherent cells' features, which makes feature extraction inadequate and the recognition rate low.  
In this paper, a deep-learning target recognition network based on Faster R-CNN is proposed in order to recognize both single cells and adherent cells in a densely distributed cell scene. On the basis of the Faster R-CNN, a balanced feature pyramid (BFP) [21] structure, deformable convolution network (DCN) [22] and efficient pyramid split attention (EPSA) mechanism [23] are adopted to form a method named BDE_RCNN. This method can effectively extract multi-scale features of cells and thus realizes the automatic recognition of binary classification for single cells and adherent cells. The proposed method has better feature extraction capability in comparison to other networks in terms of cell recognition. This will provide an effective solution for the automatic recognition of densely distributed cells in an actual medical scene.