A hybrid model for efficient cervical cell classification. (February 2022)

• Record Type: Journal Article
• Authors: K, Sabeena; C, Gopakumar
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part A
• Publication Date: 2022-02
• Subjects: PSPNet -- Cervical cell classification -- CNN -- Stack classifier; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103288
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A modified PSP segmentation model is proposed for delineating cell components. The efficacy of nuclear as well as cell features on ml classifiers is investigated. With the integration of Stack classifier, the robustness of the model is ensured. Binary and Bethesda classification are evaluated on the publicly available dataset. Abstract: Abnormal cell recognition from cervical pap cytology is crucial for early cervical cancer screening and treatment. However, the current manual analysis requires the undivided attention of the pathologists and is prone to diagnostic errors. To tackle this issue, several techniques have been proposed in the last few decades. The success of an automation-based cervical screening and diagnosis system relies on accurate identification of cell types. This paper proposes an efficient automated hybrid framework for enhancing the cell classification accuracy of cervical cytology images. In this work, a Pyramid Scene Parsing Model is used for the segmentation of cell components. The integration of the feature pyramid with local and global context prior makes the model suitable for the segmentation of small cell components. Nuclear as well as cell features of segmented regions are extracted and categorized using an ensemble-based stack classifier. The performance of five different machine learning classifiers is also investigated and it was found that the CNN-based segmentation followed by ensemble-based stack classifier achieved the best performance. The model was evaluated on a publicly available data set and achieved an average accuracy and AUC of 99.7% and 0.996 for 2-class classification and 75.55% and 0.909 for 4-class classification respectively. The experiments demonstrate that the proposed framework achieves promising performance on pap stained cytology images.