Comparative Analysis of CNN Methods for Periapical Radiograph Classification

Made Windu Antara Kesiman; I Made Gede Sunarya; I Gusti Lanang Trisna Sumantara

doi:10.23887/janapati.v13i2.71664

Authors

Made Windu Antara Kesiman Universitas Pendidikan Ganesha
I Made Gede Sunarya Universitas Pendidikan Ganesha
I Gusti Lanang Trisna Sumantara Universitas Pendidikan Ganesha

DOI:

https://doi.org/10.23887/janapati.v13i2.71664

Keywords:

Periapical radiographs, Convolutional Neural Network (CNN), Primary Endodontic Lesion, F1-Score, Precision, EfficientNetB1, ResNet50v2, MobileNet

Abstract

Periapical radiographs are commonly used by dentists to diagnose dental problems and overall dental health conditions. The varying abilities of dentists to diagnose may be limited by their visual acuity and individual skills. To address this issue, there is a need for an application capable of computationally recognizing and classifying periapical radiographs. The commonly used computational method for image processing, specifically image recognition, is the Convolutional Neural Network (CNN) method. This study aims to create an application that can classify periapical radiographs and analyze the capabilities of the Convolutional Neural Network (CNN) method in this classification process. In general, periapical classification is divided into five types: Primary Endo with Secondary Perio, Primary Endodontic Lesion, Primary Perio with Secondary Endo, Primary Periodontal Lesion, and True Combined Lesions. The periapical radiograph classification process was tested using four CNN models: ResNet50v2, EfficientNetB1, MobileNet, and Shalow CNN. The evaluation of the CNN method utilized a confusion matrix-based technique to generate accuracy, precision, recall, F1-score and Weighted Average F1-score values. Based on the evaluation results, the highest accuracy value was achieved by EfficientNetB1 with 82%, followed by ResNet50v2 with 76%, MobileNet with 75%, and Shallow CNN with 71%.

References

R. Ismail Hardianzah, B. Hidayat, F. Teknik Elektro, And U. Telkom Jl Telekomunikasi Terusan Buah Batu Bandung, “Pengolahan Citra Radiograf Periapikal Pada Deteksi Penyakit Pulpitis Menggunakan Metode Adaptive Region Growing Approach,” 2017.

M. Louisa, “Lesi Endoperio 1,” 2015.

B. Indra And S. Huldani, “Radiografi Gigi Dan Imunitas Serluler Trombosit Hemoglobin Leukosit”,2019.

C. W. Li Et Al., “Detection Of Dental Apical Lesions Using Cnns On Periapical Radiograph,” Sensors, Vol. 21, No. 21, Nov. 2021, Doi: 10.3390/S21217049.

S. M. R And M. Tech Student, “Classification Of Dental Disease Using Cnn,” 2020. [Online]. Available: Http://Ijesc.Org/

University Of Buner. Department Of Electronics & Computer Science And Institute Of Electrical And Electronics Engineers, 1st International Conference On Electrical, Communication And Computer Engineering (Icecce 2019) : 24th - 25th July 2019, Swat, Pakistan.

A. Thomas, R. N. Firman, And A. Azhari, “Analisis Radiograf Periapikal Menggunakan Software Imagej Pada Granuloma Periapikal Pada Perawatan Endodontik,” Majalah Kedokteran Gigi Indonesia, Vol. 3, No. 2, P. 105, Dec. 2017, Doi: 10.22146/Majkedgiind.10472.

M. Tan And Q. V Le, “Efficientnet: Rethinking Model Scaling For Convolutional Neural Networks”,2019.

Fangyuan Lei, Xun Liu, Jianjian Jiang, Qingyun Dai, Hongyu Liu, Mengying Hu.” Shallow Convolutional Neural Network for Image Recognition”,2019.Doi: 10.17706/ijcee.2019.11.4.192-197

Agung Wahyu Setiawan.” Comparison Of Convolutional Neural Network Architecture In The Classification Of Pneumonia, Covid-19, Lung Opacity, And Normal Using Thorax X-Ray Image”,2022. DOI: 10.25126/jtiik.202296742.

Made Windu Antara Kesiman; Kadek Teguh Dermawan; I Gede Mahendra Darmawiguna. “Balinese Carving Ornaments Classification Using InceptionResnetV2 Architecture”,2023. DOI:10.1109/CENIM56801.2022.10037265.

Yuni Naomi Yenusi, Suryasatriya Trihandaru, Adi Setiawan.” Perbandingan Model Convolutional Neural Network pada Klasifikasi Wajah Orang Papua dan Etnis Lainnya”,2022. https://doi.org/10.23887/jstundiksha.v12i1.46861

Zhafeni Arif, R. Yunendah Nur Fu’adah, Syamsul Rizal, Divo Ilhamdi. “Classification of eye diseases in fundus images using convolutional neural network (CNN) method with efficientnet architecture”,2023.DOI: http://dx.doi.org/10.29210/02020344.

Ching-Chen Wang, Ching-Te Chiu, Jheng-Yi Chang. “EfficientNet-eLite: Extremely Lightweight and Efficient CNN Models for Edge Devices by Network Candidate Search”,2020. DOI:https://doi.org/10.48550/arXiv.2009.07409

Abdul Rafay a, Waqar Hussain. “EfficientSkinDis: An EfficientNet-based classification model for a large manually curated dataset of 31 skin diseases”,2023. DOI: https://doi.org/10.1016/j.bspc.2023.104869.