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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