Cervical cancer identification using deep learning approaches

Kong, Shien Nie and Handayani, Dini Oktarina Dwi and Mun, Hou Kit and Chong, Pei Pei and Mantoro, Teddy (2022) Cervical cancer identification using deep learning approaches. In: 8th International Conference on Computing, Engineering, and Design (ICCED 2022), Sukabumi, Indonesia (Virtual Conference).

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Abstract

The presence of cervical cancer is not apparent as its incubation period is long. A pap smear screening is the only diagnostic method; examining the pap smear slides uses a microscope. However, problems happen where humans make mistakes during the diagnostic process, causing inaccurate results and delaying the individual who needs to receive comprehensive treatments. With hopes to improve the current situation, assisting cervical cancer diagnosis with artificial intelligence techniques is suggested. This paper will use ResNet101v2, an upgraded residual network from ResNet, to develop a cervical cancer detection model to predict the severity of cervical cells. The Herlev dataset distributed 70% into training and 30% into validation; remaining 98 unique images will be used during the testing stage. Transfer learning techniques were introduced to develop the model. Using the testing images, the model reached 71.4% accuracy contributing better accuracy compared to other research studies in predicting the cervical cells using 7 classification classes. The model shows potential for clinical cytotechnologists’ during the pap smear diagnosis.

Item Type:	Proceeding Paper (Other)
Uncontrolled Keywords:	Artificial Intelligence, Cervical cancer, Deep Learning
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Dini Handayani
Date Deposited:	01 Feb 2023 09:15
Last Modified:	23 Jan 2024 11:30
URI:	http://irep.iium.edu.my/id/eprint/103436

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