[Deep learning network-based recognition and localization of diatom images against complex background].

ABSTRACT

ObjectiveWe propose a deep learning network-based method for recognizing and locating diatom targets with interference by complex background in autopsy.MethodThe system consisted of two modules the preliminary positioning module and the accurate positioning module. In preliminary positioning, ZFNet convolution and pooling were utilized to extract the high-level features, and Regional Proposal Network (RPN) was applied to generate the regions where the diatoms may exist. In accurate positioning, Fast R-CNN was used to modify the position information and identify the types of the diatoms.ResultsWe compared the proposed method with conventional machine learning methods using a self-built database of images with interference by simple, moderate and complex backgrounds. The conventional methods showed a recognition rate of diatoms against partial background interference of about 60%, and failed to recognize or locate the diatom objects in the datasets with complex background interference. The deep learning network-based method effectively recognized and located the diatom targets against complex background interference with an average recognition rate reaching 85%.ConclusionThe proposed method can be applied for recognition and location of diatom targets against complex background interference in autopsy.