Skripsi

LOKALISASI CITRA PRA-KANKER SERVIKS MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN VISUALISASI GUIDED BACKPROPAGATION

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Penilaian

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The aim of this study is to perform the localization of pre-cervical cancer images using Convolutional Neural Network (CNN) method and guided backpropagation visualization. The pre-cervical cancer image data is evaluated with various CNN models that implement the second type of data augmentation. The research findings show that the CervicoNet model with epoch 200, learning rate 10-3 , and batch size 16 achieved the highest accuracy of 98.96%. The model that provided the best unseen accuracy is the CervicoNet model with epoch 150, learning rate 10-3 , and batch size 16, with an accuracy of 82.29% on previously unseen data. The best visualization results were obtained from the CervicoNet model with epoch 150, learning rate 10-3 , and batch size 16. This research is expected to contribute to the management of pre-cervical cancer through the use of deep learning techniques and guided backpropagation visualization for the localization of pre-cervical cancer images with improved accuracy

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002761	T122544	T1225442023	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1225442023

Publisher

Indralaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xvi, 74 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.507

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Sistem Komputer
Storage

Specific Detail Info

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Statement of Responsibility

FIRA

Other version/related

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