Blood Leukocyte Object Detection According to Model Parameter-Transfer and Deformable Convolution

Currently, leukocyte detection has the problem of scarcity of labeled samples, so a focal dataset must be expanded by merging multiple datasets. At the same time, given the difference in the dyeing methods, dyeing time, and collection techniques, some datasets have the problem of different homology distributions. Moreover, the effect of direct training after dataset merging is not satisfactory. The morphology of the leukocyte types is also variable and stain contamination occurs, thereby leading to the misjudgment of using traditional convolutional networks. Therefore, in this paper, the model parameter-transfer method is used to alleviate the problem of less leukocyte labeled data in the training model and deformable convolution is introduced into the main network of target detection to improve the accuracy of the object detection model. First, numerous leukocyte datasets are used to train the blood leukocyte binary classification detection network, and the model parameters of the blood leukocyte binary classification detection network are transferred to the blood leukocyte multi classification detection network through the transfer of model parameters. This method can make better use of datasets of the same origin and different distributions so as to solve the problem of scarcity in blood leukocyte data sets. Finally, the multi classification detection network is trained quickly and the accurate blood leukocyte detection results are obtained through fine tuning. The experimental results show that compare our method with the traditional Faster RCNN object detection algorithm, \({mAP}_{0.5}\) is 0.056 higher, \({mAP}_{0.7}\) is 0.119 higher, with higher recall by 4%, and better accuracy by 5%. Thus, the method proposed in this paper can achieve highly accurate leukocyte detection.