Convolutional Neural Networks Based on Raspberry Pi for a Prototype of Vocal Cord Abnormalities Identification

Main Article Content

Hertiana Bethaningtyas D.K.
Muhammad Agfian Fadillah
Lulu Millatina Rachmawati
Maiisy Jahja
Asep Suhendi


This study aims to make a device prototype for identifying vocal cord abnormalities based on Raspberry Pi. This prototype could classify the abnormalities into seven classes, i.e., cysts, granulomas, nodules, normal, papilloma, paralysis, and no vocal cords. The applied method to classify is a deep learning algorithm, mainly using Convolutional Neural Network (CNN). In building the CNN model, we used a statistical method to form a model training scenario, also modified the AlexNet architecture model by optimizing the parameters. The optimized parameters in the test scenario obtained 95.35% accuracy. The CNN model implemented on the Raspberry Pi, and the test results obtained 79.75% accuracy.


Download data is not yet available.

Article Details

How to Cite
H. B. D.K., M. A. Fadillah, L. M. Rachmawati, M. Jahja, and A. Suhendi, “Convolutional Neural Networks Based on Raspberry Pi for a Prototype of Vocal Cord Abnormalities Identification”, INFOTEL, vol. 12, no. 3, pp. 82-88, Aug. 2020.


[1] M. Ester and A. S. Augustinus, Sistem pernapasan dan sistem kardiovaskular. 1999.
[2] Anies, Seri kesehatan umum pencegahan dini gangguan kesehatan: berbagai penyakit dan gangguan kesehatan yang perlu diwaspadai dan dicegah secara dini. Jakarta: Pt Elex Media Komputindo, 2005.
[3] D. E. Newman-Toker Et Al., “Serious misdiagnosis-related harms in malpractice claims: the ‘big three’–vascular events, infections, and cancers,” Diagnosis, vol. 6, pp. 227–240, 2019.
[4] Docdoc, “What is stroboscopy: overview, benefits, and expected results,” 2020. Https:==Www:Docdoc:Com=Medicalinformation=Procedures=Stroboscopy.
[5] A. Kadir and A. Susanto, Teori dan aplikasi pengolahan citra digital. Yogyakarta: Andi, 2013.
[6] Young, I. T., Gerbrands, J. J., Vliet, and L. J. Van., Fundamentals Of Image Processing. 2007.
[7] B. G. L. Pubiyangga, “Identifikasi kondisi pita suara untuk deteksi kelainan pita suara dengan metode moore neighbor tracing,” Univ. Telkom, P. 2016.
[8] H. Bethaningtyas, S. Suwandi, and C. D. Anggraini, “Sistem klasifikasi kondisi pita suara dengan metode decision tree,” J. Nas. Tek. Elektro Dan Teknol. Inf., vol. 8, no. 2, pp. 168, 2019, Doi: 10.22146/Jnteti.V8i2.506.
[9] H. Bethanigtyas, Suwandi, and C. D. Anggraini, “Classification System Vocal Cords Disease Using Digital Image Processing,” Proc. - 2019 Ieee Int. Conf. Ind. 4.0, Artif. Intell. Commun. Technol. Iaict 2019, no. C, pp. 129–132, 2019, Doi: 10.1109/Iciaict.2019.8784832.
[10] C. D. Anggraini, “Identifikasi otomatis kelainan pada pita suara menggunakan teknologi pengolahan citra digital.,” Telkom University, 2019.
[11] B. Luan, Y. Sun, C. Tong, Y. Liu, and H. Liu, “R-FCN based laryngeal lesion detection,” Proc. - 2019 12th Int. Symp. Comput. Intell. Des. Isc. 2019, pp. 128–131, 2019, Doi: 10.1109/Iscid.2019.10112.
[12] C. Matava, E. Pankiv, S. Raisbeck, M. Caldeira, and F. Alam, “A convolutional neural network for real time classification, identification, and labelling of vocal cord and ttacheal using laryngoscopy and bronchoscopy video,” J. Med. Syst., vol. 44, no. 2, 2020, Doi: 10.1007/S10916-019-1481-4.
[13] S. Albawi, T. A. M. Mohammed, and S. Alzawi, “A data-driven approach to precipitation parameterizations using convolutional encoder-decoder neural networks pablo,” IEEE, 2017, [Online]. Available:
[14] Z. C. Horn, L. Auret, J. T. McCoy, C. Aldrich, and B. M. Herbst, “Performance of convolutional neural networks for feature extraction in froth flotation sensing,” IFAC-PapersOnLine, vol. 50, no. 2, pp. 13–18, 2017, doi: 10.1016/j.ifacol.2017.12.003.
[15] B. Santosa and A. Umam, Data mining dan big data analysis, 2nd ed. Penerbit Penebar Media Pustaka: Yogyakarta., 2018.
[16] Chennupati and Sumanth., “Hierarchical decomposition of large deep networks.,” Rochester Institute of Technology, 2016.