Classification of Cervical Cancer Images Using Deep Residual Network Architecture

(1) * Hilman Fauzi Mail (Telkom University, Indonesia)
(2) Revydo Bima Ansori Mail (Telkom University, Indonesia)
(3) Thomhert Siadari Mail (Telkom University, Indonesia)
(4) Ali Budi Harsono Mail (Universitas Padjadjaran, Indonesia)
(5) Qisthi Nur Rahmah Mail (Telkom University, Indonesia)
*corresponding author


According to data from the World Health Organization (WHO), cervical cancer is ranked second, with a high mortality rate in women every year. Cervical cancer is caused by the presence of the Human Papilloma Virus (HPV), which directly attacks the cervix. Additionally, an unhealthy lifestyle can cause attacks of this disease. Several methods can be used to detect cervical cancer early, one of which is Visual Inspection with Acetic Acid (VIA). Through VIA, tests can determine whether patients are infected with the HPV virus. The results of the VIA test can be seen with the naked eye, but medical experts have different opinions about the diagnosis made using their vision. Therefore, to assist medical practitioners in diagnosing the results of VIA, an examination with a technological approach was carried out. Digital imagery was used for the analysis. A medical expert’s Android camera was used with .jpg image format to capture pictures of the VIA test results. In this study, cervical cancer image classification was carried out from the results of the VIA test examination that had been carried out at Hasan Sadikin Hospital, Bandung, with as many as 255 data points for Negative VIA and 65 data points for Positive VIA. In the image processing of the VIA test results, CLAHE images and Canny Edge Detection images are used. Deep learning was used with the ResNet-50 and ResNet-101 architectural models to classify images, and different hyperparameter configurations, such as optimizers, learning rates, batch sizes, and input sizes, were tested. In this study, the best results were obtained using Canny Edge Detection images with hyperparameter configurations using the SGD optimizer with a learning rate of 0.1, a batch size of 32, and an input size of 224 × 224.


Cervical Cancer, Digital Image Processing, VIA Examination, Canny Edge Detection, ResNet



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R. Elakkiya, V. Subramaniyaswamy, V. Vijayakumar, and A. Mahanti, “Cervical Cancer Diagnostics Healthcare System Using Hybrid Object Detection Adversarial Networks,†IEEE J. Biomed. Heal. Informatics, vol. 26, no. 4, pp. 1464–1471, 2022.

Juwitasari, R. Harini, and A. A. Rosyad, “Husband Support Mediates the Association between Self-Efficacy and Cervical Cancer Screening among Women in the Rural Area of Indonesia,†Asia-Pacific J. Oncol. Nurs., vol. 8, no. 5, pp. 560–564, 2021.

E. S. Aoki et al., “National screening programs for cervical cancer in Asian countries,†J. Gynecol. Oncol., vol. 31, no. 3, pp. 1–9, 2020.

G. Namale et al., “Visual inspection with acetic acid (VIA) positivity among female sex workers: a cross-sectional study highlighting one-year experiences in early detection of pre-cancerous and cancerous cervical lesions in Kampala, Uganda,†Infect. Agent. Cancer, vol. 16, no. 1, pp. 1–11, 2021.

D. Endarti, Satibi, S. A. Kristina, M. A. Farida, Y. Rahmawanti, and T. Andriani, “Knowledge, perception, and acceptance of HPV vaccination and screening for cervical cancer among women in Yogyakarta Province, Indonesia,†Asian Pacific J. Cancer Prev., vol. 19, no. 4, pp. 1105–1111, 2018.

K. E. Wijayanti, H. Schütze, and C. MacPhail, “Parents’ attitudes, beliefs and uptake of the school-based human papillomavirus (HPV) vaccination program in Jakarta, Indonesia – A quantitative study,†Prev. Med. Reports, vol. 24, p. 101651, 2021.

Y. Xiang, W. Sun, C. Pan, M. Yan, Z. Yin, and Y. Liang, “ScienceDirect A novel automation-assisted cervical cancer reading method based on convolutional neural network,†Integr. Med. Res., vol. 40, no. 2, pp. 611–623, 2020.

A. Ajit and A. C. Layer, “A Review of Convolutional,†pp. 1–5, 2020.

A. P. Coding, “ResNet-Like Belief-Propagation Decoding for Polar Codes,†vol. 10, no. 5, pp. 2021–2024, 2021.

T. Alaoui et al., “Classification of chest pneumonia from x-ray images using new architecture based on ResNet,†2021.

A. Mahajan, “Categorical Image Classification Based On Representational Deep Network ( RESNET ),†2019 3rd Int. Conf. Electron. Commun. Aerosp. Technol., pp. 327–330, 2019.

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.

A. Çınar, M. Yıldırım, and Y. Eroğlu, “Classification of pneumonia cell images using improved ResNet50 model,†Trait. du Signal, vol. 38, no. 1, pp. 165–173, 2021.

Faiz Nashrullah, Suryo Adhi Wibowo, and Gelar Budiman, “The Investigation of Epoch Parameters in ResNet-50 Architecture for Pornographic Classification,†J. Comput. Electron. Telecommun., vol. 1, no. 1, pp. 1–8, 2020.

F. Ri et al., “/ Deho , Pdjh & Odvvlilfdwlrq % Dvhg Rq 7Udqvihu,†pp. 354–358, 2020.

O. Data, “Technical counseling of data processing with spss 25 for some employees of the bkad office of majene district,†vol. 9, no. 3, pp. 184–187, 2020.

B. K. Umri, M. W. Akhyari, and K. Kusrini, “Detection of Covid-19 in Chest X-ray Image using CLAHE and Convolutional Neural Network,†pp. 14–18, 2021.

G. J. H. Hwhfwlrq et al., “7KH 5HVHDUFK RI , PSOHPHQWDWLRQ 0HWKRG RI & DQQ ,†pp. 20–23, 2020.

S. Bera and V. K. Shrivastava, “Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification,†Int. J. Remote Sens., vol. 41, no. 7, pp. 2664–2683, 2020.

Z. Zhang, “Improved Adam Optimizer for Deep Neural Networks,†2018 IEEE/ACM 26th Int. Symp. Qual. Serv., pp. 1–2, 2018.

B. I. N. Xiao, Y. Liu, and B. Xiao, “Accurate State-of-Charge Estimation Approach for Lithium-Ion Batteries by Gated Recurrent Unit With Ensemble Optimizer,†IEEE Access, vol. 7, pp. 54192–54202, 2019.

N. Shirish and K. Richard, “Improving Generalization Performance by Switching from Adam to SGD,†no. 1, 2017.

N. O. Attoh-okine, “Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance,†vol. 30, pp. 291–302, 1999.

S. Edition, Deep Learning with Python.

S. Rajbhandari and J. Rasley, “ZeRO : Memory Optimizations Toward Training Trillion Parameter Models,†pp. 1–24.

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