KLASIFIKASI BATU GINJAL PADA CITRA CT MENGGUNAKAN METODE CNN MODEL VGG16

AKBAR, MUHAMMAD FAISAL and Fachrurrozi, Muhammad (2024) KLASIFIKASI BATU GINJAL PADA CITRA CT MENGGUNAKAN METODE CNN MODEL VGG16. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, CNN, VGG16, Batu Ginjal
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Faisal Akbar
Date Deposited:	20 Aug 2024 01:52
Last Modified:	20 Aug 2024 01:52
URI:	http://repository.unsri.ac.id/id/eprint/155711

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