Computer-Aided Cervical Cancer Diagnosis Using Time-Lapsed Colposcopic Images

Li, Yuexiang; Chen, Jiawei; Xue, Peng; Tang, Chaohua; Jia, Chang; Chu, Chunyan; Ma, Kai; Li, Qing; Zheng, Yefeng; Qiao, You‐Lin

doi:10.1109/tmi.2020.2994778

Cited by 92 publications

(51 citation statements)

References 37 publications

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“…The CAIADS algorithm consists of two deep-learning-based modules for grading colposcopic impressions and guiding biopsies, respectively. A detailed description of the CAIADS algorithm is presented in Additional file 1, Supplementary Method and Figure S3 [18][19][20]. Briefly, the proposed CAIADS first detected the cervical area of images for the subsequent feature extraction.…”

Section: Development Of the Caiads Algorithmmentioning

confidence: 99%

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies

Xue

Tang

et al. 2020

BMC Med

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Background Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) for grading colposcopic impressions and guiding biopsies. Methods Anonymized digital records of 19,435 patients were obtained from six hospitals across China. These records included colposcopic images, clinical information, and pathological results (gold standard). The data were randomly assigned (7:1:2) to a training and a tuning set for developing CAIADS and to a validation set for evaluating performance. Results The agreement between CAIADS-graded colposcopic impressions and pathology findings was higher than that of colposcopies interpreted by colposcopists (82.2% versus 65.9%, kappa 0.750 versus 0.516, p < 0.001). For detecting pathological high-grade squamous intraepithelial lesion or worse (HSIL+), CAIADS showed higher sensitivity than the use of colposcopies interpreted by colposcopists at either biopsy threshold (low-grade or worse 90.5%, 95% CI 88.9–91.4% versus 83.5%, 81.5–85.3%; high-grade or worse 71.9%, 69.5–74.2% versus 60.4%, 57.9–62.9%; all p < 0.001), whereas the specificities were similar (low-grade or worse 51.8%, 49.8–53.8% versus 52.0%, 50.0–54.1%; high-grade or worse 93.9%, 92.9–94.9% versus 94.9%, 93.9–95.7%; all p > 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758. Conclusions The CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer.

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Section: Development Of the Caiads Algorithmmentioning

confidence: 99%

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies

Xue

Tang

et al. 2020

BMC Med

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“…However, it is the third most common cause of death among the female genital cancers next to ovarian and cervical cancers. A Computer Aided Diagnosis (CAD) systems [1], [2]can aid the doctors to predict and diagnose the cervical cancer in the early stage.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Boykov's graph cuts based segmentation for Cervical Cancer Detection

Balaji¹,

Suryanarayana²,

Sengathir³

2021

EAI Endorsed Trans Perv Health Tech

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Introduction: Cervical cancer is considered as the major curse to women society of the world due to their low survival rate. However, the prognosis of cervical cancer at an early stage through periodic screening was identified to enhance the survival probability of the women patients around the world. Objective: In this paper, a Boykov-Kolmogorov Graph Cuts and Cloud Model-based Synergy Integrated Segmentation (BKGC-CMSIS) Technique for facilitating predominant cervical cancer detection from the pap smear images used for prognosis. Methods: This proposed BKGC-CMSIS scheme introduces an effective Boykov-Kolmogorov Graph Cuts-based image partitioning method that estimates the image data through a synergy cloud model for formulating objective functions. The objective function used in this proposed BKGC-CMSIS scheme includes a data item and a smooth term for boundary preservation in order to determine the deviation of each pixel corresponding to the different regions of the cervical pap smear cells. Results: Also, it identifies the data item through the utilization of X-condition cloud generator for determining and defining the accurate boundaries of cytoplasm and nuclei derived from the pap smear cells. This proposed BKGC-CMSIS scheme uses the merits of membership degree through the incorporation of the smooth term for estimating the degree of similarity existing among the neighboring regions of the cervical pap smear cells. Conclusion: The experimental results of this proposed BKGC-CMSIS scheme is also potent in enhancing the classification accuracy by 14% superior to the benchmarked cervical cancer detection schemes considered for investigation.

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“…The ensemble approach using Bagging method attained a precision of 98.12% with higher f-measure, accuracy, and recall values. In Li et al [10], they presented a deep learning (DL) architecture for the precise recognitions of Low-Grade Squamous Intraepithelial Lesion (LSIL) (includes cervical cancer and Cervical intraepithelial neoplasia (CIN)) with time-lapsed colposcopic image. The presented architecture includes 2 major modules, viz., feature fusion network and key frame feature encoding network.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Abukhalil¹,

Alqaralleh²,

Alomari³

2022

Computers, Materials &Amp; Continua

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Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature vectors. Moreover, swallow swarm optimization (SSO) based hyperparameter tuning process is carried out for the optimal selection of hyperparameters. Finally, recurrent neural network (RNN) based classification process is done to determine the presence of cervical cancer or not. In order to showcase the improved diagnostic performance of the ODLIM-CCD technique, a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.

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Computer-Aided Cervical Cancer Diagnosis Using Time-Lapsed Colposcopic Images

Cited by 92 publications

References 37 publications

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies

Enhanced Boykov's graph cuts based segmentation for Cervical Cancer Detection

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

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