Cervical cell classification with deep-learning algorithms

• Published in: Medical & biological engineering & computing Vol. 61; no. 3; pp. 821 - 833
• Main Authors: Xu, Laixiang, Cai, Fuhong, Fu, Yanhu, Liu, Qian
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Cancer; Cervical cancer; Classification; Computer Applications; data collection; Data processing; Deep Learning; Diagnosis; Female; Generative adversarial networks; Human Physiology; Humans; Image analysis; Image processing; Image Processing, Computer-Assisted - methods; Imaging; Learning; Localization; Machine learning; Medical imaging; Neural Networks, Computer; Object recognition; Original Article; Radiology; spectral analysis; uterine cervical neoplasms; Uterine Cervical Neoplasms - diagnosis; Womens health; Deep learning; Cervical cell classification; Pathology images
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-022-02745-3

Cervical cancer is a serious threat to the lives and health of women. The accurate analysis of cervical cell smear images is an important diagnostic basis for cancer identification. However, pathological data are often complex and difficult to analyze accurately because pathology images contain a wide variety of cells. To improve the recognition accuracy of cervical cell smear images, we propose a novel deep-learning model based on the improved Faster R-CNN, shallow feature enhancement networks, and generative adversarial networks. First, we used a global average pooling layer to enhance the robustness of the data feature transformation. Second, we designed a shallow feature enhancement network to improve the localization and recognition of weak cells. Finally, we established a data augmentation network to improve the detection capability of the model. The experimental results demonstrate that our proposed methods are superior to CenterNet, YOLOv5, and Faster R-CNN algorithms in some aspects, such as shorter time consumption, higher recognition precision, and stronger adaptive ability. Its maximum accuracy is 99.81%, and the overall mean average precision is 89.4% for the SIPaKMeD and Herlev datasets. Our method provides a useful reference for cervical cell smear image analysis. The missed diagnosis rate and false diagnosis rate are relatively high for cervical cell smear images of different pathologies and stages. Therefore, our algorithms need to be further improved to achieve a better balance. We will use a hyperspectral microscope to obtain more spectral data of cervical cells and input them into deep-learning models for data processing and classification research. Graphical Abstract First, we sent training samples of cervical cells into our proposed deep-learning model. Then, we used the proposed model to train eight types of cervical cells. Finally, we utilized the trained classifier to test the untrained samples and obtained the classification results. Fig 1. Deep-learning cervical cell classification framework