DETECTION OF CERVICAL CANCER FROM UTERINE CERVIX IMAGES USING TRANSFER LEARNING ARCHITECTURES

Yıl 2024, , 222 - 239, 28.06.2024

Hanife Göker

https://doi.org/10.18038/estubtda.1384489

Öz

Cervical cancer is a common and serious cancer affecting more than half a million women worldwide. For cervical cancer disease management, prognosis prediction, or optimizing medical intervention, early detection of the disease is critical. It is one of the types of cancer that can be successfully treated, as long as it is diagnosed early and managed effectively. In this study, an image processing-based solution was proposed for the diagnosis of cervical cancer from uterine cervix images using transfer learning architectures to reduce the workload and assist the experts. The proposed transfer learning model was tested using a publicly available dataset, which includes 917 uterine cervix images. Uterine cervix images were enhanced and brightness level using the histogram equalization method and denoised using the Gaussian filter. Then, the performances of AlexNet, DenseNet201, MobilenetV2, Resnet50, Xception, and VGG19 transfer learning architectures were compared. The transfer learning model performance was evaluated using the 10-fold cross-validation method. VGG19 transfer learning algorithm had the highest performance. VGG19 transfer learning algorithm achieved 98.26% accuracy, 0.9671 f1-measure, 0.9896 specificity, 0.9631 sensitivity, 0.9711 precision, 0.9552 Matthews correlation coefficient (MCC), and 0.955 kappa statistic. The combination of histogram equalization, Gaussian filter, and the VGG19 transfer learning approach can be used for accurate and efficient detection of cervical cancer from uterine cervix images. In this study, more accuracy was achieved compared to the known related studies in the literature.

Anahtar Kelimeler

Image processing, Transfer learning, Deep learning, Cervix cancer, Gaussian filter

Kaynakça

• [1] Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 2021; 71: 3, 209-249.
• [2] Siegel RL, Miller KD, Jemal A. Cancer statistics 2019. CA: A Cancer Journal for Clinicians 2019; 69: 1, 7-34.
• [3] World Health Organization. Projections of mortality and causes of death 2015 and 2030. Online available: https://www.who.int/health-topics/cervical-cancer#tab=tab_1. [Accessed: 02-January-2023]
• [4] Arora A, Tripathi A, Bhan A. Classification of cervical cancer detection using machine learning algorithms. In 2021 6th International Conference on Inventive Computation Technologies (ICICT) 2021; pp. 827-835.
• [5] Freeman HP, Wingrove BK. Excess cervical cancer mortality: a marker for low access to health care in poor communities. National Cancer Institute, Center to Reduce Cancer Health Disparities Rockville, MD. 2005.
• [6] Li C, Xue D, Zhou X, Zhang J, Zhang H, Yao Y, Kong F, Zhang L, Sun, H. Transfer learning based classification of cervical cancer immunohistochemistry images. In Proceedings of the Third International Symposium on Image Computing and Digital Medicine 2019; pp. 102-106.
• [7] Sompawong N. Automated pap smear cervical cancer screening using deep learning. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019; pp. 7044-7048.
• [8] Cohen PA, Jhingran A, Oaknin A, Denny L. Cervical cancer. The Lancet 2019; 393: 10167, 169-182.
• [9] Tao L, Amanguli A, Li F, Wang YH, Y, Yang, L, Mohemaiti M, Zhao J, Zou XG, Saimaiti A, Abudu M, Maimaiti M, Chen SY, Abudukelimu R, Maimati A, Li SG, Zhang W, Aizimu AA, Yang AQ, Wang J, Pang LJ, Cao YG, Gu WY, Zhang WJ. Cervical screening by Pap test and visual inspection enabling same-day biopsy in low-resource, high-risk communities. Obstetrics & Gynecology 2018; 132: 6, 1421-1429.
• [10] Chandran V, Sumithra MG, Karthick A, George T, Deivakani M, Elakkiya B, Subramaniam U, Manoharan S. Diagnosis of cervical cancer based on ensemble deep learning network using colposcopy images 2021; BioMed Research International. 5584004: 1-15.
• [11] Cibula D, Pötter R, Planchamp F, Avall-Lundqvist E, Fischerova D, Haie-Meder C, Köhler C, Landoni F, Lax S, Lindegaard JC, Mahantshetty U, Mathevet P, McCluggage WG, McCormack M, Naik R, Nout R, Pignata S, Ponce J, Querleu D, Raspagliesi F, Rodolakis A, Tamussino K, Wimberger P, Raspollini MR. The european society of gynaecological oncology/european society for radiotherapy and oncology/european society of pathology guidelines for the management of patients with cervical cancer. Virchows Archiv 2018; 472: 919-936.
• [12] Luo W.Predicting cervical cancer outcomes: statistics, images, and machine learning. Frontiers in Artificial Intelligence 2021; 4: 627369, 1-5.
• [13] Nayar S, Panicker JV, Nair JJ. Deep learning based model for multi-class classification of cervical cells using pap smear images. In 2022 IEEE 7th International Conference for Convergence in Technology (I2CT) 2022; pp. 1-6.
• [14] Ming Y, Dong X, Zhao J, Chen Z, Wang H, Wu N. Deep learning-based multimodal image analysis for cervical cancer detection. Methods 2022; 205: 46-52.
• [15] Ahishakiye E, Wario R, Mwangi W, Taremwa D. Prediction of cervical cancer basing on risk factors using ensemble learning,” In 2020 IST-Africa Conference (IST-Africa) 2020; pp. 1-12.
• [16] Saini SK, Bansal V, Kaur R, Juneja M. ColpoNet for automated cervical cancer screening using colposcopy images. Machine Vision and Applications 2020; 31: 1-15.
• [17] Priyanka BJ, Raju B. Machine learning approach for prediction of cervical cancer. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 2021; 12: 8, 3050-3058.
• [18] Mehmood M, Rizwan M, Gregus ml M, Abbas S. Machine learning assisted cervical cancer detection. Frontiers in Public Health 2021; 9: 788376, 1-14.
• [19] Alsmariy R, Healy G, Abdelhafez H. Predicting cervical cancer using machine learning methods. International Journal of Advanced Computer Science and Applications 2020; 11(7).173-184
• [20] Kalbhor M M., Shinde S V. Cervical cancer diagnosis using convolution neural network: feature learning and transfer learning approaches. Soft Computing 2023, 1-11.
• [21] Jantzen J, Dounias G. Analysis of pap-smear image data. The database can be downloaded at https://mde-lab.aegean.gr/index.php/downloads/. In Proceedings of the Nature-Inspired Smart Information Systems 2nd Annual Symposium 2006; 10: 1-11.
• [22] Mustafa WA, Kader MMMA. A review of histogram equalization techniques in image enhancement application. In Journal of Physics: Conference Series 2018; 1019: 1, pp. 1-7.
• [23] Dhal KG, Das A, Ray S, Gálvez J, Das S. Histogram equalization variants as optimization problems: a review. Archives of Computational Methods in Engineering 2021; 28: 3, 1471-1496.
• [24] Escorcia-Gutierrez J, Mansour RF, Beleño K, Jiménez-Cabas J, Pérez M, Madera N, Velasquez K. Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images. Computers, Materials and Continua 2022; 71: 3, 4221-4235.
• [25] Han X, Zhong Y, Cao L, Zhang L. Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sensing 2017; 9: 848, 1-22.
• [26] Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017; pp. 4700-4708.
• [27] Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv 2017; 1704.04861.
• [28] Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018; pp. 4510-4520.
• [29] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016; pp. 770- 778.
• [30] Chollet F. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017; pp. 1251-1258.
• [31] Zheng Y, Yang C, Merkulov A. Breast cancer screening using convolutional neural network and follow-up digital mammography. In Computational Imaging III 2018; 10669: 1066905.
• [32] Cantor AB. Sample-size calculations for Cohen's kappa. Psychological Methods 1996; 1: 2, 150-153.
• [33] Çimen E. A transfer learning approach by using 2-d convolutional neural network features to detect unseen arrhythmia classes. Eskişehir Technical University Journal of Science and Technology A-Applied Sciences and Engineering 2021; 22.1: 1-9.
• [34] Sun G, Li S, Cao Y, Lang F. Cervical cancer diagnosis based on random forest. International Journal of Performability Engineering 2017; 13: 4, 446-457.
• [35] Malli PK, Nandyal S. Machine learning technique for detection of cervical cancer using k-NN and artificial neural network. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) 2017; 6: 4, 145-149.
• [36] Nguyen LD, Gao R, Lin D, Lin Z. Biomedical image classification based on a feature concatenation and ensemble of deep CNNs. Journal of Ambient Intelligence and Humanized Computing 2019. 1-13.
• [37] Lin H, Hu Y, Chen S, Yao J, Zhang L. Fine-grained classification of cervical cells using morphological and appearance based convolutional neural networks. IEEE Access 2019. 7: 71541-71549.
• [38] Khamparia A, Gupta D, de Albuquerque VHC, Sangaiah AK, Jhaveri RH. Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning. The Journal of Supercomputing 2020; 76: 11, 8590-8608.
• [39] Ravindran K, Rajkumar S, Muthuvel K. An investigation on cervical cancer with image processing and hybrid classification. International Journal of Performability Engineering 2021; 17: 11, 918-925.
• [40] Lavanya Devi N, Thirumurugan P. Cervical cancer classification from pap smear images using modified fuzzy C means, PCA, and KNN. IETE Journal of Research 2022; 68: 3, 1591-1598.
• [41] Shinde S, Kalbhor M, Wajire P. DeepCyto: A hybrid framework for cervical cancer classification by using deep feature fusion of cytology images. Math. Biosci. Eng 2022; 19: 6415-6434.
• [42] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv 2014; 1409.1556.
• [43] Tammina S. Transfer learning using VGG-16 with deep convolutional neural network for classifying images. International Journal of Scientific and Research Publications (IJSRP) 2019; 9: 10, 143-150.