In Computer methods and programs in biomedicine
PURPOSE : To propose a fast detection method for prostate cancer abnormal cells based on deep learning. The purpose of this method is to quickly and accurately locate and identify abnormal cells, so as to improve the efficiency of prostate precancerous screening and promote the application and popularization of prostate cancer cell assisted screening technology.
METHOD : The method includes two stages: preliminary screening of abnormal cell images and accurate identification of abnormal cells. In the preliminary screening stage of abnormal cell images, ResNet50 model is used as the image classification network to judge whether the local area contains cell clusters. In the another stage, YoloV5 model is used as the target detection network to locate and recognize abnormal cells in the image containing cell clusters.
RESULTS : This detection method aims at the pathological cell images obtained by the membrane method. And the double stage models proposed in this paper are compared with the single stage model method using only the target detection model. The results show that through the image classification network based on deep learning, we can first judge whether there are abnormal cells in the local area. If there are abnormal cells, we can further use the target detection method based on candidate box for analysis, which can reduce the reasoning time by 50% and improve the efficiency of abnormal cell detection under the condition of losing a small amount of accuracy and slightly increasing the complexity of the model.
CONCLUSION : This study proposes a fast detection method for prostate cancer abnormal cells based on deep learning, which can greatly shorten the reasoning time and improve the detection speed. It is able to improve the efficiency of prostate precancerous screening.
Huang Hongyuan, You Zhijiao, Cai Huayu, Xu Jianfeng, Lin Dongxu
2022-Oct-18
Convolutional neural network, Deep learning, Image classification, Pathological image, Prostate cancer cell detection
