CNN KLASIFIKASI TINGKAT KEPARAHAN PRA KANKER SERVIKS PADA CITRA VIA MENGGUNAKAN CNN

AMBARSARI, ALYA NUR FIRJATULLAH and Nurmaini, Siti (2023) CNN KLASIFIKASI TINGKAT KEPARAHAN PRA KANKER SERVIKS PADA CITRA VIA MENGGUNAKAN CNN. Undergraduate thesis, Sriwijaya University.

	Text RAMA_56201_09011381924125.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy
	Text RAMA_56201_09011381924125_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (11MB) | Request a copy
	Text RAMA_56201_09011381924125_0002085908_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB)
	Text RAMA_56201_09011381924125_0002085908_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (290kB) | Request a copy
	Text RAMA_56201_09011381924125_0002085908_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (562kB) | Request a copy
	Text RAMA_56201_09011381924125_0002085908_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011381924125_0002085908_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (11kB) | Request a copy
	Text RAMA_56201_09011381924125_0002085908_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (146kB) | Request a copy
	Text RAMA_56201_09011381924125_0002085908_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (907kB) | Request a copy

Abstract

The aim of this study is to classify cervicalpre cancer images using Convolutional Neural Network (CNN) method. The dataset of cervical precancer images was evaluated using various CNN models that implemented data augmentation of the second type. The research findings indicate that the CervicoNet model with 200 epochs, a learning rate of 10-3, and a batch size of 32 achieved the best accuracy of 93.75%. The model that provided the best unseen accuracy was the ResNet50 model with 100 epochs, a learning rate of 10-3, and a batch size of 32, achieving 68.88% accuracy on unseen data. The top three class accuracy results were obtained using the VGG 19 model with 100 epochs, a learning rate of 0.001, and a batch size of 32, achieving an accuracy of 99.6%. The results of this study are expected to contribute to the handling of cervical precancer through the application of deep learning techniques for the localization of cervical precancer images with improved accuracy.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Convolutional Neural Networks (CNN), klasifikasi, Tingkat keparahan kanker Serviks, Citra Pra-Kanker Serviks.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Alya Nur Firjatullah Ambarsari
Date Deposited:	08 Aug 2023 02:44
Last Modified:	08 Aug 2023 02:44
URI:	http://repository.unsri.ac.id/id/eprint/126222

Actions (login required)

View Item