###### Abstract  
This paper proposes a novel Squeeze-and-excitation-based Mask Region Convolutional Neural Network (SEM-RCNN) for Environmental Microorganisms (EM) detection tasks. Mask RCNN, one of the most applied object detection models, uses ResNet for feature extraction. However, ResNet cannot combine the features of different image channels. To further optimize the feature extraction ability of the network, SEM-RCNN is proposed to combine the different features extracted by SENet and ResNet. The addition of SENet can allocate weight information when extracting features and increase the proportion of useful information. SEM-RCNN achieves a mean average precision (mAP) of 0.511 on EMDS-6. We further apply SEM-RCNN for blood-cell detection tasks on an open source database (more than 17,000 microscopic images of blood cells) to verify the robustness and transferability of the proposed model. By comparing with other detectors based on deep learning, we demonstrate the superiority of SEM-RCNN in EM detection tasks. All experimental results show that the proposed SEM-RCNN exhibits excellent performances in EM detection. environmental microorganisms; object detection; deep learning +
Footnote †: journal: Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China 1
Manual microscope observation methods refer to the observation and record of EMs in the field of view by an experimenter with certain professional knowledge using a microscope. However, there exist some limitations and disadvantages with respect to manual microscope observation methods. First, the experimenter cannot make quick judgments and must consult many reference materials when facing a wide variety of EMs. Second, all experimenters have to spend a substantial amount of time when learning the basics of EMs and the operation of the microscope. Finally, the detection results obtained by different operators might be different, and the objectivity of the detection results is insufficient [4]. Therefore, manual microscope observation methods have great limitations for EM detection tasks. Compared to manual microscope observation methods, computer-aided detection methods are more objective, accurate, and convenient. With rapid developments in computer vision and deep learning technologies, computer-assisted image analysis is broadly applied in many research fields, including fire emergency [5], histopathological image analysis [6; 7; 8; 9], cytopathological image analysis [10; 11; 12], object detection [13; 14; 15; 16; 17], microorganism classification [18; 19; 20; 21; 22; 23], microorganism segmentation [24; 25; 26; 27], and microorganism counting [28; 29]. In addition, with the advancement of computer hardware and the rapid development of computer-aided detection methods, the results obtained by computer-aided detection methods in EM detection are improving. Currently, the most popular computer-aided detection method is the EM detection method based on deep learning [30]. However, there is no relevant research on the detection of multi-class EMs. Therefore, we choose some classical detectors based on deep learning for multi-class EM detection and propose a novel detector called squeeze-and-excitation-based mask region convolutional neural network (SEM-RCNN). The flowchart of SEM-RCNN is shown in Figure 1.  
In Figure 1, three main parts are contained. Part one is the original dataset part, which includes enough images of EMs for model training and testing. We will introduce the specific information about the original dataset in detail in the experimental section. Part two is the data-processing part. In this part, the original dataset is firstly labeled in the format of the object detection dataset. Then, all data are grouped into the training set, validation set, and test set according to a certain proportion. Part three is the EM detection part. Firstly, the original SEM-RCNN is pre-trained on Microsoft Common Objects in Context (MS-COCO) dataset.