Automated Detection of Cervical Pre-Cancerous Lesions Using Regional-Based Convolutional Neural Network
Keywords:
Convolutional Neural Networks (CNN), CNN- Architecture, Cervical Colposcopy, Regional Based Convolutional Neural Network (R-CNN)Abstract
The Cervical Colposcopy image is an image of woman’s cervix taken with a digital colposcope after application of acetic acid. The captured cervical images must be understood for diagnosis, prognosis and treatment planning of the anomalies. This Cervix image understanding is generally performed by skilled medical professionals. However, the scarcity of human medical experts and the fatigue and rough estimate procedures involved with them limit the effectiveness of image understanding performed by skilled medical professionals. This paper, the model uses Regional Based Convolutional Neural Network (R-CNN) to effectively visualize of pre-cancerous lesions and to aid in diagnosis of the disease. The model was trained, on a dataset comprising of 10,383 cervical images samples. The datasets were derived from public dataset repositories. The training samples comprised of type class 1, 2 and 3 traits of cervical precancerous traits. The performance was evaluated using K-nearest -neighbor model over R-CNN. With an accuracy rate of 86%, this approach heralds a promising development in the detection of cervical precancerous lesions. This study findings established that the proposed model in provision of the better accuracy and misclassifications performance than various testing algorithms.
References
. F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA. Cancer J. Clin., vol. 68, no. 6, pp. 394–424, 2018, doi: 10.3322/caac.21492.
. F. Islami, L. A. Torre, J. M. Drope, E. M. Ward, and A. Jemal, “Global cancer in women: Cancer control priorities,” Cancer Epidemiol. Biomarkers Prev., vol. 26, no. 4, pp. 458–470, 2017, doi: 10.1158/1055-9965.EPI-16-0871.
. World Health Organization, GUIDE TO CANCER - Guide to cancer early diagnosis. 2017.
. “Kenya Cancer Statistics & National Strategies,” Kenyan Network of Cancer Organizations, Feb. 18, 2013. https://kenyacancernetwork.wordpress.com/kenya-cancer-facts/ (accessed Aug. 13, 2020).
. “(1) (PDF) Screening for Cervical Cancer Using Automated Analysis of PAP-Smears,” ResearchGate. https://www.researchgate.net/publication/261923436_Screening_for_Cervical_Cancer_Using_Automated_Analysis_of_PAP-Smears (accessed Aug. 14, 2020).
. “Wei et al. - 2017 - Cervical cancer histology image identification met.pdf.” .
. X. Q. Zhang and S. G. Zhao, “Cervical image classification based on image segmentation preprocessing and a CapsNet network model,” Int. J. Imaging Syst. Technol., vol. 29, no. 1, pp. 19–28, 2019, doi: 10.1002/ima.22291.
. “National-Cancer-Screening-Guidelines-2018.pdf.” Accessed: Aug. 13, 2020. [Online]. Available: https://www.health.go.ke/wp-content/uploads/2019/02/National-Cancer-Screening-Guidelines-2018.pdf.
. L. Wei, Q. Gan, and T. Ji, “Cervical cancer histology image identification method based on texture and lesion area features,” Comput. Assist. Surg., vol. 22, no. sup1, pp. 186–199, Oct. 2017, doi: 10.1080/24699322.2017.1389397.
. “Intel & MobileODT Cervical Cancer Screening.” https://kaggle.com/c/intel-mobileodt-cervical-cancer-screening (accessed Aug. 14, 2020).
. World Health Organization, Ed., WHO guidelines for screening and treatment of precancerous lesions for cervical cancer prevention. Geneva: World Health Organization, 2013.
. K. Fernandes, D. Chicco, J. S. Cardoso, and J. Fernandes, “Supervised deep learning embeddings for the prediction of cervical cancer diagnosis,” PeerJ Comput. Sci., vol. 4, p. e154, May 2018, doi: 10.7717/peerj-cs.154.
. “Maini and Aggarwal - 2010 - A Comprehensive Review of Image Enhancement Techni.pdf.” .
. “(1) (PDF) A Novel Analysis of Clinical Data and Image Processing Algorithms in Detection of Cervical Cancer,” ResearchGate. https://www.researchgate.net/publication/277667329_A_Novel_Analysis_of_Clinical_Data_and_Image_Processing_Algorithms_in_Detection_of_Cervical_Cancer (accessed Aug. 14, 2020).
. M. Sato et al., “Application of deep learning to the classification of images from colposcopy,” Oncol. Lett., Jan. 2018, doi: 10.3892/ol.2018.7762.
. N. Muinga et al., “Digital health Systems in Kenyan Public Hospitals: a mixed-methods survey,” BMC Med. Inform. Decis. Mak., vol. 20, no. 1, p. 2, Dec. 2020, doi: 10.1186/s12911-019-1005-7.
. M. Nielsen, “Neural networks and deep learning.” 2019.
. A. Mittal and M. Juneja, “Cervix Cancer Classification using Colposcopy Images by Deep Learning Method,” Int. J. Eng. Technol. Sci. Res., vol. 5, no. 3, pp. 426–432, 2018.
. C. Data Science, “An Intuitive Explanation of Convolutional Neural Networks – the data science blog_.” 2017.
. R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights Imaging, vol. 9, no. 4, pp. 611–629, 2018, doi: 10.1007/s13244-018-0639-9.
. H. A. Almubarak et al., “Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images.,” Procedia Comput. Sci., vol. 114, pp. 281–287, 2017, doi: 10.1016/j.procs.2017.09.044.
. Guillaume Berger, “CS231n Convolutional Neural Networks for Visual Recognition.” 2016.
. J. Brownlee, “A Gentle Introduction to the Rectified Linear Unit (ReLU),” Machine Learning Mastery, Jan. 08, 2019. https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/ (accessed Aug. 14, 2020).
. “CS231n Convolutional Neural Networks for Visual Recognition.” https://cs231n.github.io/convolutional-networks/ (accessed Aug. 14, 2020).
. “Convolutional Neural Network. In this article, we will see what are… | by Arunava | Towards Data Science.” https://towardsdatascience.com/convolutional-neural-network-17fb77e76c05 (accessed Aug. 14, 2020).
. J. Brownlee, “A Gentle Introduction to Pooling Layers for Convolutional Neural Networks,” Machine Learning Mastery, Apr. 21, 2019. https://machinelearningmastery.com/pooling-layers-for-convolutional-neural-networks/ (accessed Aug. 14, 2020).
. “Litjens et al. - 2017 - A Survey on Deep Learning in Medical Image Analysi.pdf.” .
. J. Tompson, A. Jain, Y. LeCun, and C. Bregler, “Joint training of a convolutional network and a graphical model for human pose estimation,” Adv. Neural Inf. Process. Syst., vol. 2, no. January, pp. 1799–1807, 2014.
. M. R. Minar and J. Naher, “Recent Advances in Deep Learning: An Overview,” vol. 2006, pp. 1–31, 2018, doi: 10.13140/RG.2.2.24831.10403.
. M. Wu, C. Yan, H. Liu, Q. Liu, and Y. Yin, “Automatic classification of cervical cancer from cytological images by using convolutional neural network,” Biosci. Rep., vol. 38, no. 6, pp. 1–9, 2018, doi: 10.1042/BSR20181769.
. “Detecting Driver Drowsiness in Real Time Through Deep Learning Based Object Detection,” springerprofessional.de. https://www.springerprofessional.de/en/detecting-driver-drowsiness-in-real-time-through-deep-learning-b/16772484 (accessed Aug. 14, 2020).
. Prabhu, “Understanding of Convolutional Neural Network (CNN) — Deep Learning,” Medium, Nov. 21, 2019. https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148 (accessed Aug. 27, 2020).
. G. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis,” Med. Image Anal., vol. 42, pp. 60–88, Dec. 2017, doi: 10.1016/j.media.2017.07.005.
. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” ArXiv151200567 Cs, Dec. 2015, Accessed: Aug. 15, 2020. [Online]. Available: http://arxiv.org/abs/1512.00567.
. R. Barth, J. Hemming, and E. J. Van Henten, “Optimising realism of synthetic images using cycle generative adversarial networks for improved part segmentation,” Comput. Electron. Agric., vol. 173, p. 105378, Jun. 2020, doi: 10.1016/j.compag.2020.105378.
. E. Supriyanto, N. A. M. Pista, L. H. Ismail, B. Rosidi, and T. L. Mengko, “Automatic Detection System of Cervical Cancer Cells Using Color Intensity Classification,” p. 5.
. G. K. Lakshmi and K. Krishnaveni, “Multiple Feature Extraction from Cervical Cytology Images by Gaussian Mixture Model,” in 2014 World Congress on Computing and Communication Technologies, Feb. 2014, pp. 309–311, doi: 10.1109/WCCCT.2014.89.
. S. Roy, R. P. Chauhan, and G. K. Verma, “Cervical cancer detection from MR images based on multiresolution wavelet analysis,” in 2016 11th International Conference on Industrial and Information Systems (ICIIS), Dec. 2016, pp. 788–793, doi: 10.1109/ICIINFS.2016.8263046.
. S. Garg, S. Urooj, and R. Vijay, “Detection of cervical cancer by using thresholding watershed segmentation,” in 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), Mar. 2015, pp. 555–559.
. S. Bosse, D. Maniry, T. Wiegand, and W. Samek, “Fraunhofer Institute for Telecommunications , Heinrich Hertz Institute , Berlin , Germany Department of Electrical Engineering , Technical University of Berlin , Germany .,” pp. 1–5.
. “Find Open Datasets and Machine Learning Projects | Kaggle.” https://www.kaggle.com/datasets (accessed Aug. 14, 2020).
. K. Grill-Spector, K. S. Weiner, J. Gomez, A. Stigliani, and V. S. Natu, “The functional neuroanatomy of face perception: from brain measurements to deep neural networks,” Interface Focus, vol. 8, no. 4, p. 20180013, Aug. 2018, doi: 10.1098/rsfs.2018.0013.
. “(1) Multimodal Deep Learning for Cervical Dysplasia Diagnosis | Request PDF,” ResearchGate. https://www.researchgate.net/publication/308816998_Multimodal_Deep_Learning_for_Cervical_Dysplasia_Diagnosis (accessed Aug. 14, 2020).
. T. Xu et al., “Multi-feature based Benchmark for Cervical Dysplasia Classification Evaluation,” Pattern Recognit., vol. 63, pp. 468–475, Mar. 2017, doi: 10.1016/j.patcog.2016.09.027.
. “Bergstra and Bengio - Random Search for Hyper-Parameter Optimization.pdf.” Accessed: Aug. 14, 2020. [Online]. Available: https://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf.
. M. Z. Alom, M. Hasan, C. Yakopcic, and T. M. Taha, “Inception Recurrent Convolutional Neural Network for Object Recognition,” ArXiv170407709 Cs, Apr. 2017, Accessed: Aug. 14, 2020. [Online]. Available: http://arxiv.org/abs/1704.07709.
. G. Larsson, M. Maire, and G. Shakhnarovich, “FractalNet: Ultra-Deep Neural Networks without Residuals,” ArXiv160507648 Cs, May 2017, Accessed: Aug. 14, 2020. [Online]. Available: http://arxiv.org/abs/1605.07648.
. “(1) (PDF) Neural Network Based Rhetorical Status Classification for Japanese Judgment Documents,” ResearchGate. https://www.researchgate.net/publication/338402067_Neural_Network_Based_Rhetorical_Status_Classification_for_Japanese_Judgment_Documents (accessed Aug. 14, 2020).
. “(1) (PDF) Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review.” https://www.researchgate.net/publication/317496930_Deep_Convolutional_Neural_Networks_for_Image_Classification_A_Comprehensive_Review (accessed Aug. 14, 2020).
. J. Liu, Y. Yang, S. Lv, J. Wang, and H. Chen, “Attention-based BiGRU-CNN for Chinese question classification,” J. Ambient Intell. Humaniz. Comput., Jun. 2019, doi: 10.1007/s12652-019-01344-9.
. “(1) (PDF) Performance Analysis of Deep Learning CNN Models for Disease Detection in Plants using Image Segmentation.” https://www.researchgate.net/publication/337355921_Performance_Analysis_of_Deep_Learning_CNN_Models_for_Disease_Detection_in_Plants_using_Image_Segmentation (accessed Aug. 14, 2020).
. J. Görtler, R. Kehlbeck, and O. Deussen, “A Visual Exploration of Gaussian Processes,” Distill, vol. 4, no. 4, p. e17, Apr. 2019, doi: 10.23915/distill.00017.
. L. Rampasek and A. Goldenberg, “TensorFlow: Biology’s Gateway to Deep Learning?,” Cell Syst., vol. 2, no. 1, pp. 12–14, Jan. 2016, doi: 10.1016/j.cels.2016.01.009.
. X. Zhang and S.-G. Zhao, “Cervical image classification based on image segmentation preprocessing and a CapsNet network model,” Int. J. Imaging Syst. Technol., vol. 29, no. 1, pp. 19–28, Mar. 2019, doi: 10.1002/ima.22291.
. “(1) (PDF) Application of deep learning to the classification of images from colposcopy,” ResearchGate. https://www.researchgate.net/publication/322368324_Application_of_deep_learning_to_the_classification_of_images_from_colposcopy (accessed Aug. 14, 2020).
. “Feature selection considering two types of feature relevancy and feature interdependency | Expert Systems with Applications: An International Journal.” https://dl.acm.org/doi/10.1016/j.eswa.2017.10.016 (accessed Aug. 14, 2020).
. “[1411.6228] From Image-level to Pixel-level Labeling with Convolutional Networks.” https://arxiv.org/abs/1411.6228 (accessed Aug. 14, 2020).
. X. Q. Zhang and S. G. Zhao, “Cervical image classification based on image segmentation preprocessing and a CapsNet network model,” Int. J. Imaging Syst. Technol., vol. 29, no. 1, pp. 19–28, 2019, doi: 10.1002/ima.22291.
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers with this journal agree to the following terms.
