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[17]K. V., P. K., and G. S., “Andriod Device-Based Cervical Cancer Screening for Resource-Poor Settings,” J. Digit. Imaging, vol. 31, no. 5, pp. 646–654, 2018.
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[19]H. H. Son, P. C. Phuong, T. Van Walsum, and L. Manh Ha, “Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components,” VNU J. Sci. Comput. Sci. Commun. Eng., vol. 36, no. 1, pp. 25–37, 2020.
[20]M. Aghalari, A. Aghagolzadeh, and M. Ezoji, “Brain tumor image segmentation via asymmetric/symmetric Unet based on two-pathway-residual blocks,” Biomed. Signal Process. Control, 2021.
[21]J. Wu and C. Hicks, “Breast cancer type classification using machine learning,” J. Pers. Med., vol. 11, no. 2, pp. 1–12, 2021.
[22]O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” pp. 1–8.
[23]E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” vol. 8828, no. c, pp. 1–12, 2016.
[24]M. N. Rachmatullah, S. Nurmaini, A. I. Sapitri, A. Darmawahyuni,B. Tutuko, and F. Firdaus, “Convolutional neural network for semantic segmentation of fetal echocardiography based on four-chamber view,” Bull. Electr. Eng. Informatics, vol. 10, no. 4, pp. 1987–1996, 2021.
[2]A. Srivastava, P. Sinha, P. Vatsal, F. Khatoon, and N. Lal, “Visual Inspection with Acetic Acid Versus Papanicolaou Test in Cervical Cancer Screening,” Indian J. Gynecol. Oncol., 2020.
[3]J. Liu, Y. Peng, and Y. Zhang, “A Fuzzy Reasoning Model for Cervical Intraepithelial Neoplasia Classification Using Temporal Grayscale Change and Textures of Cervical Images During Acetic Acid Tests,” IEEE Access, 2019.
[4]V. Kudva, K. Prasad, and S. Guruvare, “Detection of Specular Reflection and Segmentation of Cervix Region in Uterine Cervix Images for Cervical Cancer Screening,” Irbm, vol. 38, no. 5, pp. 281–291, 2017.
[5]J. Liu, L. Li, and L. Wang, “Acetowhite region segmentation in uterine cervix images using a registered ratio image,” 2017.
[6]K. Gutiérrez-fragoso, H. G. Acosta-mesa, N. Cruz-ramírez, and R. Hernández-jiménez, “Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy,” Hindawi, vol. 2017, 2017.
[7]V. Kudva, K. Prasad, and S. Guruvare, “Automation of detection of cervical cancer using convolutional neural networks,” Crit. Rev. Biomed. Eng., vol. 46, no. 2, pp. 135–145, 2018.
[8]J. Lu, E. Song, A. Ghoneim, and M. Alrashoud, “Machine learning for assisting cervical cancer diagnosis : An ensemble approach,” Futur. Gener. Comput. Syst., vol. 106, pp. 199–205, 2020.
[9]M. Sharma, S. Kumar Singh, P. Agrawal, and V. Madaan, “Classification of Clinical Dataset of Cervical Cancer using KNN,” Indian J. Sci. Technol., vol. 9, no. 28, 2016.
[10]W. William, A. Ware, A. H. Basaza-Ejiri, and J. Obungoloch, “A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images,” Comput. Methods Programs Biomed., vol. 164, pp. 15–22, 2018.
[11]Y. Song et al., “Accurate cervical cell segmentation from overlapping clumps in pap smearimages,” IEEE Trans. Med. Imaging, vol. 36, no. 1, pp. 288–300, 2017.
[12]K. M. A. Adweb, N. Cavus, and B. Sekeroglu, “Cervical Cancer Diagnosis Using Very Deep Networks over Different Activation Functions,” IEEE Access, vol. 9, pp. 46612–46625, 2021.
[13]M. N. Asiedu et al., “Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket Colposcope HHS Public Access,” IEEE Trans Biomed Eng, vol. 66, no. 8, pp. 2306–2318, 2019.
[14]B. Bai, Y. Du, P. Liu,P. Sun, P. Li, and Y. Lv, “Detection of cervical lesion region from colposcopic images based on feature reselection,” Biomed. Signal Process. Control, vol. 57, p. 101785, 2020.
[15]H. L. Holgersti-medicalcom, “Automatic detection of multi-level acetowhite regions in RGB color images of the uterine cervix,” vol. 5747, pp. 1004–1017, 2005.
[16]T. Xu, E. Kim, and X. Huang, “ADJUSTABLE ADABOOST CLASSIFIER AND PYRAMID FEATURES FOR IMAGE-BASED CERVICAL CANCER DIAGNOSIS Computer Science and Engineering Department , Lehigh University , Bethlehem , PA , USA ;,” pp. 281–285, 2015.
[17]K. V., P. K., and G. S., “Andriod Device-Based Cervical Cancer Screening for Resource-Poor Settings,” J. Digit. Imaging, vol. 31, no. 5, pp. 646–654, 2018.
[18]V. Kudva, K. Prasad, and S. Guruvare, “Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening,” J. Digit. Imaging, vol. 33, no. 3, pp. 619–631, 2020.
[19]H. H. Son, P. C. Phuong, T. Van Walsum, and L. Manh Ha, “Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components,” VNU J. Sci. Comput. Sci. Commun. Eng., vol. 36, no. 1, pp. 25–37, 2020.
[20]M. Aghalari, A. Aghagolzadeh, and M. Ezoji, “Brain tumor image segmentation via asymmetric/symmetric Unet based on two-pathway-residual blocks,” Biomed. Signal Process. Control, 2021.
[21]J. Wu and C. Hicks, “Breast cancer type classification using machine learning,” J. Pers. Med., vol. 11, no. 2, pp. 1–12, 2021.
[22]O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” pp. 1–8.
[23]E. Shelhamer, J. Long, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” vol. 8828, no. c, pp. 1–12, 2016.
[24]M. N. Rachmatullah, S. Nurmaini, A. I. Sapitri, A. Darmawahyuni,B. Tutuko, and F. Firdaus, “Convolutional neural network for semantic segmentation of fetal echocardiography based on four-chamber view,” Bull. Electr. Eng. Informatics, vol. 10, no. 4, pp. 1987–1996, 2021.
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Affiliations
Akhiar Wista Arum
Universitas Sriwijaya
Siti Nurmaini
Universitas Sriwijaya
Dian Palupi Rini
Universitas Sriwijaya
Patiyus Agustiansyah
Department of Obstetric and Gynecology, Division Oncology of Gynecology, Faculty of Medicine, Universitas Sriwijaya, Palembang
Muhammad Naufal Rachmatullah
Affiliation not stated
Segmentation of Squamous Columnar Junction on VIA Images using U-Net Architecture
Abstract
Cervical cancer is the second most common cancer that affects women, especially in developing countries including Indonesia. Cervical cancer is a type of cancer found in the cervix, precisely in the squamous columnar junction (SCJ). Early screening for cervical cancer can be reduce the risk of cervical cancer. One of the popular screening tool methods for the detection of cervical pre-cancer is the Visual Inspection with Acetic Acid (VIA) method. This is due to the level of effectiveness, convenience and low cost. This paper proposes a method for the detection and segmentation of the SCJ region on VIA images using U-Net. This study is the first research conducted using the CNN method to perform segmentation tasks in the SCJ region. The best performance results are shown from the Pixel Accuracy, Mean IoU, Mean Accuracy, Dice coefficient, Precision and Sensitivity values, namely 90.86%, 56.5%, 75.69%, 34.09%, 41.24%, and 56.91%.
Keywords: Cervical Pre-cancer, Screening VIA, SCJ, U-Net.
Keywords: Cervical Pre-cancer, Screening VIA, SCJ, U-Net.