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KLASIFIKASI KONDISI GIGI BERDASARKAN CITRA RGB MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS

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Penilaian

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This study utilizes a deep learning model using a Convolutional Neural Network with ResNet – 18 architecture in classifying dental conditions based on RGB images consisting of four classes: Calculus, Caries, Gingivitis, and Tooth Discoloration. The data used came from a collection of several dental and oral diseases. The preprocessing stages include Resize, Center Crop, Random Resized Crop, Random Horizontal Flip, Random Rotation, To Tensor, and Normalize. The model training process using hyperparameters includes Epoch 50, Learning Rate 0.001, Batch Size 16, and Optimizer Adam. The research conducted the implementation of Residual Network -18. The results of the model evaluation had a fairly high accuracy performance in the Caries class with an accuracy score of 97% and the Tooth Discoloration class with an accuracy of 96%. However, the accuracy performance was low in the Gingivitis class of 88% and the Calculus class of 78%. Differences in accuracy performance between classes are due to the characteristics of the dataset or when distinguishing visual features between classes. This research provides a practical approach and contributes to improving efficiency in the diagnosis of dental conditions, especially reducing considerable radiation exposure in patients during X-Ray examinations. By using deep learning methods, this approach has the potential to speed up the diagnosis process, while offering safer and more practical solutions for doctors and patients. This not only improves the accuracy in detecting dental conditions but also supports more effective medical practices in the future.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000582	T165189	T1651892024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1651892024

Publisher

Magister Ilmu Komputer, Fakult : .,

Collation

xiii, 48 hlm.: ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

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)

Sistem komputer
Magister Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

TUTI

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