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All Journal Bulletin of Electrical Engineering and Informatics Sinergi Jurnal Teknologi Elektro Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Abdi Masyarakat (JAM)
Zendi Iklima
Universitas Mercu Buana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Dental caries classification using depthwise separable convolutional neural network for teledentistry system Trie Maya Kadarina; Zendi Iklima; Rinto Priambodo; Riandini Riandini; Rika Novita Wardhani
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4428

Abstract

Caries may be halted or reversed in their progression by early detection, better hygiene habits, and coadministered drugs. The major clinical procedures for identifying dental caries are visual-tactile examination and dental radiography. However, due to their location, approximate caries exceedingly difficult to detect and affect the clinical assessment. Incorrect interpretations may also hinder the diagnostic procedure. Computational approaches and technology can be used to help dentists assess caries. Teledentistry has the ability to improve dental health care by providing access to dental care services from a remote location. Teledentistry helps identifying various stages of caries lesions using neural network and devices connected to the internet. This research develops an image classification for teledentistry systems using depthwise separable convolutional neural network. The trainable parameters reduction of depthwise separable convolution (DSC) successfully reduces the computational cost of conventional convolutional neural networks (CNN). As a result, the DSC model is reduced by 91.49% when compared to the traditional CNN model. Several DSC models improve conventional CNN accuracies in the training, validation, evaluation, and testing stages.
Co-Authors Annisa Ayu Wardani Fina Supegina Jody Maulana Anggi Julpri Andika Kadarina, Trie Maya Ketty Siti Salamah Muhammad Hafizd Ibnu Hajar Rachmat Muwardi Riandini Riandini Rika Novita Wardhani Rinto Priambodo Suwoyo, Heru Triyanto Pangaribowo Yudhi Gunardi