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Critical Reviews™ in Biomedical Engineering

Published 6 issues per year

ISSN Print: 0278-940X

ISSN Online: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

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Automation of Detection of Cervical Cancer Using Convolutional Neural Networks

Volume 46, Issue 2, 2018, pp. 135-145
DOI: 10.1615/CritRevBiomedEng.2018026019
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ABSTRACT

Classification of digital cervical images acquired during visual inspection with acetic acid (VIA) is an important step in automated image-based cervical cancer detection. Many algorithms have been developed for classification of cervical images based on extracting mathematical features and classifying these images. Deciding the suitability of a feature and learning the algorithm is a complex task. On the other hand, convolutional neural networks (CNNs) self-learn most suitable hierarchical features from the raw input image. In this paper, we demonstrate the feasibility of using a shallow layer CNN for classification of image patches of cervical images as cancerous or not cancerous. We used cervix images acquired after the application of 3%−5% acetic acid using an Android device in 102 women. Of these, 42 cervix images belonged in the VIA-positive category (pathologic) and 60 in the VIA-negative category (healthy controls). A total of 275 image patches of 15 × 15 pixels were manually extracted from VIA-positive areas, and we considered these patches as positive examples. Similarly, 409 image patches were extracted from VIA-negative areas and were labeled as VIA negative. These image patches were classified using a shallow layer CNN composed of a layer each of convolutional, rectified linear unit, pooling, and two fully connected layers. A classification accuracy of 100% is achieved using shallow CNN.

CITED BY
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  2. Kudva Vidya, Prasad Keerthana, Guruvare Shyamala, Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening, Journal of Digital Imaging, 33, 3, 2020. Crossref

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  4. Gruson Damien, Helleputte Thibault, Rousseau Patrick, Gruson David, Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation, Clinical Biochemistry, 69, 2019. Crossref

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  6. Saini Sumindar Kaur, Bansal Vasudha, Kaur Ravinder, Juneja Mamta, ColpoNet for automated cervical cancer screening using colposcopy images, Machine Vision and Applications, 31, 3, 2020. Crossref

  7. Xiang Yao, Sun Wanxin, Pan Changli, Yan Meng, Yin Zhihua, Liang Yixiong, A novel automation-assisted cervical cancer reading method based on convolutional neural network, Biocybernetics and Biomedical Engineering, 40, 2, 2020. Crossref

  8. Li Yuexiang, Chen Jiawei, Xue Peng, Tang Chao, Chang Jia, Chu Chunyan, Ma Kai, Li Qing, Zheng Yefeng, Qiao Youlin, Computer-Aided Cervical Cancer Diagnosis Using Time-Lapsed Colposcopic Images, IEEE Transactions on Medical Imaging, 39, 11, 2020. Crossref

  9. Kudva Vidya, Prasad Keerthana, Guruvare Shyamala, Machine learning approaches for acetic acid test based uterine cervix image analysis, in Computational Intelligence and Its Applications in Healthcare, 2020. Crossref

  10. Miyagi Yasunari , Takehara Kazuhiro , Miyake Takahito , Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images, Molecular and Clinical Oncology, 2019. Crossref

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  12. Li Yuexiang, Liu Zhi-Hua, Xue Peng, Chen Jiawei, Ma Kai, Qian Tianyi, Zheng Yefeng, Qiao You-Lin, GRAND: A large-scale dataset and benchmark for cervical intraepithelial Neoplasia grading with fine-grained lesion description, Medical Image Analysis, 70, 2021. Crossref

  13. Mysona David Pierce, Kapp Daniel S., Rohatgi Atharva, Lee Danny, Mann Amandeep K., Tran Paul, Tran Lynn, She Jin-Xiong, Chan John K., Applying Artificial Intelligence to Gynecologic Oncology: A Review, Obstetrical & Gynecological Survey, 76, 5, 2021. Crossref

  14. Russo Daniel P., Yan Xiliang, Shende Sunil, Huang Heng, Yan Bing, Zhu Hao, Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties, Analytical Chemistry, 92, 20, 2020. Crossref

  15. Liu Jun, Peng Yun, Zhang Yingchun, A Fuzzy Reasoning Model for Cervical Intraepithelial Neoplasia Classification Using Temporal Grayscale Change and Textures of Cervical Images During Acetic Acid Tests, IEEE Access, 7, 2019. Crossref

  16. Ditria Ellen M., Buelow Christina A., Gonzalez-Rivero Manuel, Connolly Rod M., Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective, Frontiers in Marine Science, 9, 2022. Crossref

  17. Yan Tianwei, Zhang Ning, Li Jie, Liu Wenchao, Chen He, Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application, Remote Sensing, 14, 13, 2022. Crossref

  18. Ma Jing-Hang, You Shang-Feng, Xue Ji-Sen, Li Xiao-Lin, Chen Yi-Yao, Hu Yan, Feng Zhen, Computer-aided diagnosis of cervical dysplasia using colposcopic images, Frontiers in Oncology, 12, 2022. Crossref

  19. Mahyari Tayebeh Lotfi, Dansereau Richard M., Multi‐layer random walker image segmentation for overlapped cervical cells using probabilistic deep learning methods, IET Image Processing, 16, 11, 2022. Crossref

  20. Skerrett Erica, Miao Zichen, Asiedu Mercy N., Richards Megan, Crouch Brian, Sapiro Guillermo, Qiu Qiang, Ramanujam Nirmala, Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations, BME Frontiers, 2022, 2022. Crossref

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