Skripsi

KLASIFIKASI BATU GINJAL PADA CITRA CT MENGGUNAKAN METODE CNN MODEL VGG16

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Kidney stone disease is a common health issue that can lead to serious complications if not properly treated. Early and accurate detection is crucial for effective management. Therefore, this research aims to develop software for classifying kidney stones from kidney images. This software uses the Convolutional Neural Network method with the VGG16 architecture because of its excellent performance in various image classification tasks. Classification is based on coronal and axial slice images. The dataset consists of 5162 training data, 644 validation data, and 648 testing data. Experiments showed a highest accuracy rate of 99% using pre-trained layers. Based on the analysis, it is assumed that the similarity of images and patterns between classes in the dataset affects the accuracy of image recognition.

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Availability

Detail Information

Series Title

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Call Number

T1557112024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xvi, VI-2 hlm.; ilus,; tab, 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.320 7

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Jaringan Saraf Tiruan
Prodi Tejnik Informatika

Specific Detail Info

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Statement of Responsibility

SEW

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