Pengembangan Model CNN ResNet-18 untuk Klasifikasi Kondisi Gigi Berbasis Citra RGB sebagai Solusi Diagnostik Digital
DOI:
https://doi.org/10.52436/1.jpti.568Kata Kunci:
CNN, Diagnostik Digital, Klasifikasi Gigi, ResNet-18Abstrak
Penelitian ini menggunakan model CNN ResNet-18 untuk klasifikasi kondisi gigi berbasis citra RGB dengan empat kategori: Calculus, Caries, Gingivitis, dan Tooth Discoloration. Data diambil dari dataset Oral Diseases. Teknik preprocessing mencakup Resize, Center Crop, dan Normalization. Model dilatih menggunakan optimizer Adam dengan learning rate 0.001 dan 50 epoch. Tujuan penelitian menggunakan Residual Network-18 untuk melakukan klasifikasi kondisi gigi Gingivitis, Calculus, Karies, dan Tooth Discoloration yang menggunakan data dari dataset Oral Diseases. Tahapan preprocessing mencakup Resize, Center Crop, Random Resized Crop, Random Horizontal Flip, Random Rotation, ToTensor, dan Normalization. Model dilatih dengan Hyperparameter optimizer Adam, Learning Rate 0.0001, Epochs 50, dan Batch Size 16. Hasil evaluasi model mencatat bahwa model mendapatkan performa akurasi tinggi pada kelas Caries 97% dan kelas Tooth Discoloration 96%. Namun performa akurasi yang lebih rendah pada kelas Gingivitis 88% dan kelas Calculus 78%. Penelitian ini memberikan kontribusi pada efisiensi diagnostik gigi dengan mengurangi ketergantungan pada X-Ray.
Unduhan
Referensi
A. M. Puspitasari, D. E. Ratnawati, and A. W. Widodo, “Klasifikasi Penyakit Gigi Dan Mulut Menggunakan Metode Support Vector Machine,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 2, pp. 802–810, 2018, Accessed: May 16, 2024. [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/967.
W. Y. Prihandini and A. Faizah, “Perawatan Kuretase Gingiva Pada Gigi Kaninus Kanan Rahang Atas,” Jikg (Jurnal Ilmu Kedokt. Gigi), 2022, doi: 10.23917/jikg.v5i1.19355.
I. Mayerd, “Deteksi Dini Karies Gigi Dengan Metode Fluoresen Optik Pada Saliva,” J. Tek. Its, 2023, doi: 10.12962/j23373539.v12i3.118633.
L. T. Marthinu and M. Bidjuni, “Penyakit Karies Gigi Pada Personil Detasemen Gegana Satuan Brimob Polda Sulawesi Utara Tahun 2019,” Jigim (Jurnal Ilm. Gigi Dan Mulut), 2020, doi: 10.47718/jgm.v3i2.1436.
A. Fansurna, “Pengaruh Perilaku Merokok Terhadap Kejadian Penyakit Periodontal Di Puskesmas Cempaka Kota Banjarbaru,” An-Nadaa J. Kesehat. Masy., 2023, doi: 10.31602/ann.v10i1.10182.
M. H. Habibillah, “Rancang Bangun Sistem Pakar Diagnosis Penyakit Gigi Pada Manusia Berbasis Android,” Simpatik J. Sist. Inf. Dan Inform., 2021, doi: 10.31294/simpatik.v1i2.867.
A. Kumar, H. S. Bhadauria, and A. Singh, “Descriptive Analysis of Dental X-Ray Images Using Various Practical Methods: A Review,” Peerj Comput. Sci., 2021, doi: 10.7717/peerj-cs.620.
P. A. Oakley and D. E. Harrison, “X-Ray Hesitancy: Patients’ Radiophobic Concerns Over Medical X-Rays,” Dose-Response, 2020, doi: 10.1177/1559325820959542.
H. Danial and D. Setiawati, “Convolutional Neural Network (Cnn) Based On Artificial Intelligence In Periodontal Diseases Diagnosis,” vol. 20, no. 1, 2024, doi: 10.46862/interdental.v20i1.8641.
N. K. Chowdhury, M. A. Kabir, M. M. Rahman, and N. Rezoana, “ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays,” 2020, doi: 10.7717/peerj-cs.551.
L. A. Villamizar-Martinez and J. Losey, “Assessment of the Occupational Radiation Dose From a Handheld Portable X-Ray Unit During Full-Mouth Intraoral Dental Radiographs in the Dog and the Cat – A Pilot Study,” J. Vet. Dent., 2023, doi: 10.1177/08987564231175596.
N. Hilmi, “Implementasi HE, AHE, Dan CLAHE Pada Metode Convolutional Neural Network Untuk Identifikasi Citra X-Ray Paru-Paru Normal Atau Terinfeksi Covid19,” Edu Komputika J., 2023, doi: 10.15294/edukomputika.v10i1.57237.
L. Hakim, Z. Sari, and H. Handhajani, “Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network,” J. Resti (Rekayasa Sist. Dan Teknol. Informasi), 2021, doi: 10.29207/resti.v5i2.3001.
K. Lee, S. Jung, J. Ryu, S. Shin, and J. Choi, “Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs,” J. Clin. Med., vol. 9, no. 2, p. 392, 2020, doi: 10.3390/jcm9020392.
A. Mishra, “Classification of pneumonia and COVID-19 using convolutional neural network,” Int. J. Heal. Sci. Pharm., pp. 65–88, 2023, doi: 10.47992/ijhsp.2581.6411.0110.
S. Liu et al., “Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals,” Cancer Imaging, vol. 21, no. 1, 2021, doi: 10.1186/s40644-021-00425-3.
H. Mohammad?Rahimi et al., “Deep Learning in Periodontology and Oral Implantology: A scoping Review,” J. Periodontal Res., 2022, doi: 10.1111/jre.13037.
S. Sivasundaram and C. R. Pandian, “Performance Analysis of Classification and Segmentation of Cysts in Panoramic Dental Images Using Convolutional Neural Network Architecture,” Int. J. Imaging Syst. Technol., 2021, doi: 10.1002/ima.22625.
J.-H. Lee, “Identification of Dental Implant Systems From Low-Quality and Distorted Dental Radiographs Using AI Trained on a Large Multi-Center Dataset,” Sci. Rep., 2024, doi: 10.1038/s41598-024-63422-z.
J.-Y. Cha, H. Yoon, I. Yeo, K.-H. Huh, and J. Han, “Panoptic Segmentation on Panoramic Radiographs: Deep Learning-Based Segmentation of Various Structures Including Maxillary Sinus and Mandibular Canal,” J. Clin. Med., 2021, doi: 10.3390/jcm10122577.
P. Sonavane, “A Survey on Airport Detection on Remote Sensing Images Using Deep Convolutional Neural Network,” Int. J. Res. Appl. Sci. Eng. Technol., 2019, doi: 10.22214/ijraset.2019.6070.
Z. Xiao-yi, C. Luo, B. Qiao, N. Jin, Y. Zhao, and H. Zhang, “A Deep Learning Model Using Convolutional Neural Networks for Caries Detection and Recognition With Endoscopes,” Ann. Transl. Med., 2022, doi: 10.21037/atm-22-5816.
C. Kim, H.-G. Jeong, W. Park, and D. Kim, “Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study,” Jmir Med. Informatics, 2022, doi: 10.2196/38640.
E. Y. Park, H. Cho, S. Kang, S. Jeong, and E.-K. Kim, “Caries Detection With Tooth Surface Segmentation on Intraoral Photographic Images Using Deep Learning,” BMC Oral Health, 2022, doi: 10.1186/s12903-022-02589-1.
M. F. Naufal, “Analisis Perbandingan Algoritma SVM, KNN, Dan CNN Untuk Klasifikasi Citra Cuaca,” J. Teknol. Inf. Dan Ilmu Komput., 2021, doi: 10.25126/jtiik.2021824553.
H. Hidayat, “Klasifikasi Penyakit Jantung Menggunakan Random Forest Clasifier,” J. Siskom-Kb (Sistem Komput. Dan Kecerdasan Buatan), 2023, doi: 10.47970/siskom-kb.v7i1.464.
V. Artanti, “Klasifikasi Cardiovascular Diseases Menggunakan Algoritma K-Nearest Neighbors (KNN),” Techno Com, 2024, doi: 10.62411/tc.v23i2.10061.
E. Lestari, “Prediksi Keganasan Kanker Payudara Dengan Pendekatan Machine Learning,” Jati (Jurnal Mhs. Tek. Inform., 2023, doi: 10.36040/jati.v7i3.6963.
S. Lasniari, J. Jasril, S. Sanjaya, F. Yanto, and M. Affandes, “Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra,” J. Sist. Komput. dan Inform., vol. 3, no. 4, p. 450, 2022, doi: 10.30865/json.v3i4.4167.
