Towards Accurate Cervical Cancer Detection: Leveraging Two-Stage CNNs for Pap Smear Analysis

Franklin Paucar, Carlos Bojorque, Iván Reyes-Chacón, Paulina Vizcaino-Imacaña, Manuel Morocho-Cayamcela, Manuel Morocho-Cayamcela

2024

Abstract

Cervical cancer is a type of cancer that occurs in the cervix. It is caused by the abnormal growth of cells in the cervix and is often caused by the human papillomavirus. Symptoms can include abnormal vaginal bleeding, and pelvic pain, among others. It is usually diagnosed with a pelvic exam, biopsy, and Papanicolaou or pap test. Generally, during the test, a small sample of cells is taken from the cervix and examined under a microscope to look for any abnormal cells. The test is usually done during a pelvic exam and can be done in a doctor’s office or clinic, which can cause human errors to exist and lead to a deficit of service or misdiagnosis for patients. Especially, in Ecuador, cervix cancer is the second with the most prominent incidence and mortality. One of the obstacles in Latin America to improving the number of cervix cancer screens is the amount of time needed to give results. This paper proposes a pre-trained artificial neural network and a much larger database than its paper base, this will allow us to obtain better results and a network with more accurate predictions when throwing where malignant cells could be located that could lead to cervical cancer. The process to carry it out is similar to its original process, where the analysis of the Papanicolaou tests is carried out in two stages. The first focused on finding the coordinates of the anomalous cells observed within each of the images of our dataset and the second, specializing in being able to obtain an image with a much higher resolution for each of these coordinates, thus obtaining an improvement and being able to give a much more reliable diagnosis for each of the patients.

Paper Citation

in Harvard Style

Paucar F., Bojorque C., Reyes-Chacón I., Vizcaino-Imacaña P. and Morocho-Cayamcela M. (2024). Towards Accurate Cervical Cancer Detection: Leveraging Two-Stage CNNs for Pap Smear Analysis. In Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-706-1, SciTePress, pages 219-227. DOI: 10.5220/0012735800003753

in Bibtex Style

@conference{icsoft24,
author={Franklin Paucar and Carlos Bojorque and Iván Reyes-Chacón and Paulina Vizcaino-Imacaña and Manuel Morocho-Cayamcela},
title={Towards Accurate Cervical Cancer Detection: Leveraging Two-Stage CNNs for Pap Smear Analysis},
booktitle={Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2024},
pages={219-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012735800003753},
isbn={978-989-758-706-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Towards Accurate Cervical Cancer Detection: Leveraging Two-Stage CNNs for Pap Smear Analysis
SN - 978-989-758-706-1
AU - Paucar F.
AU - Bojorque C.
AU - Reyes-Chacón I.
AU - Vizcaino-Imacaña P.
AU - Morocho-Cayamcela M.
PY - 2024
SP - 219
EP - 227
DO - 10.5220/0012735800003753
PB - SciTePress