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IMPLEMENTASI ARSITEKTUR CONVLSTM-U DALAM SEGMENTASI SEMANTIK SEL TUNGGAL KANKER SERVIKS PADA CITRA PAP SMEAR

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Penilaian

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Attempt to detect cervical cancer with pap smear examination are prone to errors because it is done manually. In this case, a medical image processing system is needed to reduce cervical cancer misdiagnosis by applying semantic image segmentation using Convolutional Neural Network (CNN) method. One of the CNN architectures that has good performance in biomedical image segmentation is U-Net. Some of the weaknesses of the U-Net architecture can be solved by ConvLSTM which is a modification of the LSTM architecture to adapt to image data. In this study, the implementation of the ConvLSTM-U architecture which is a modification of the U-Net architecture with ConvLSTM for segmentation of 4 labels on pap smear images. The stages of research are data preprocessing, training, testing, evaluation, analysis, interpretation of results, and conclusion of research results. The results of segmentation research using the Herlev dataset generate an accuracy value of 90.99%, Intersection over Union (IoU) of 63.20%, F1-Score of 75.22%, precision of 74.65%, sensitivity of 76.36%, and specificity of 93.48%. Based on the results obtained, it can be said that the ConvLSTM-U architecture is very good in performing semantic segmentation of cervical cancer single cells, the prediction results given are very close to ground truth, architecture do not cause over-segmentation, architecture are sufficient in detecting the features you want to segment however, it is still not good at detecting pixels in each class, especially in the thin cytoplasmic class.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207002967	T76966	T769662022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T769662022

Publisher

Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Sriwijaya., 2022

Collation

xii, 70 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

616.150 7

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 Matematika
Penyakit pada Darah, Hematologi

Specific Detail Info

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Statement of Responsibility

SEPTA

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