Perbandingan Metode Deep Residual Network 50 dan Deep Residual Network 152 untuk Deteksi Penyakit Pneumonia pada Manusia

RIFQI RIZQULLAH EKA PRASETYO; MUHAMMAD ICHWAN

doi:10.26760/mindjournal.v6i2.168-182

Perbandingan Metode Deep Residual Network 50 dan Deep Residual Network 152 untuk Deteksi Penyakit Pneumonia pada Manusia

RIFQI RIZQULLAH EKA PRASETYO, MUHAMMAD ICHWAN

Sari

Abstrak

Pneumonia merupakan salah satu masalah Kesehatan yang sering dijumpai dan mempunyai dampak yang signifikan di seluruh dunia. Insiden pneumonia dilaporkan meningkat sesuai dengan bertambahnya usia. Pneumonia merupakan diagnosis terbanyak ketiga. Dalam penelitian ini penulis mengidentifikasi citra paru-paru dalam bentuk citra x-ray dengan metode ResNet-50 dan ResNet-152 sebagai ekstrasi ciri dan klasifikasinya. Performa sistem diukur berdasarkan nilai akurasi, presisi, recall, dan f-measure. Eksperimen dilakukan pada dataset paru-paru dengan menggunakan dua metode tersebut dan didapatkan akurasi terbaik pada ResNet-152. Hasil menunjukkan nilai rata-rata terbaik accuracy 89,3%, precision 88,8%, recall 89,6%, dan f-measure 89%. Hasil tersebut dipengaruhi oleh jumlah dataset dari citra training, citra validation, dan citra uji.

Kata kunci: Penumonia, Deep Residual Network, RESNET-50, RESNET-152

Abstract

Pneumonia is one of the most common health problems and has a significant impact throughout the world. The incidence of pneumonia is reported to increase with age. Pneumonia is the third most common diagnosis. In this study, the authors identified lung images in the form of x-ray images using the ResNet-50 and ResNet-152 methods as feature extraction and classification. System performance is measured based on the values of accuracy, precision, recall, and f-measure. Experiments were carried out on lung datasets using these two methods and the best accuracy was obtained on ResNet-152. The results show the best average value for accuracy is 89.3%, precision is 88.8%, recall is 89.6%, and f-measure is 89%. These results are influenced by the number of datasets from training images, validation images, and test images.

Keywords: Penumonia, Deep Residual Network, RESNET-50, RESNET-152

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Referensi

Eka Putra, W. S. (2016). Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101. Jurnal Teknik ITS, 5(1). https://doi.org/10.12962/j23373539.v5i1.15696

Feriawan, J., & Swanjaya, D. (2020). Perbandingan Arsitektur Visual Geometry Group dan MobileNet Pada Pengenalan Jenis Kayu. Seminar Nasional Inovasi Teknologi, 185â€“190. https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/84

Gao, X., Yan, X., Gao, P., Gao, X., & Zhang, S. (2020). Automatic detection of epileptic seizure based on approximate entropy, recurrence quantiï¬cation analysis and convolutional neural networks. Artificial Intelligence in Medicine, 102, 101711. https://doi.org/10.1016/j.artmed.2019.101711

Hakim, L. (2018). Seloka : Jurnal Pendidikan Bahasa dan Sastra Indonesia Learning Writing Explanatory Text Using Group Investigation Models Based on Learning Style. Seloka: Jurnal Pendidikan Bahasa Dan Sastra Indonesia, 7(3), 259â€“266.

Halprin Abhirawa, Jondri, A. A. (2017). Pengenalan Wajah Menggunakan Convolutional Neural Network. 4(3), 4907â€“4916.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770â€“778. https://doi.org/10.1109/CVPR.2016.90

Jimmi Lin, H. I. (2021). Klasifikasi Pneumonia Pada Citra X-Rays Paru-Paru Menggunakan GLCM Dan LVQ 1,2. 1(2), 184â€“194.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436â€“444. https://doi.org/10.1038/nature14539

Maysanjaya, I. M. D. (2020). Klasifikasi Pneumonia pada Citra X-rays Paru-paru dengan Convolutional Neural Network. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 9(2), 190â€“195. https://doi.org/10.22146/jnteti.v9i2.66

Munir, K., Elahi, H., Ayub, A., Frezza, F., & Rizzi, A. (2019). Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers, 11(9). https://doi.org/10.3390/cancers11091235

Pardede, J., & Husada, M. (2016). Comparison of VSM, GVSM, and LSI in information retrieval for indonesian text. Jurnal Teknologi, 78. https://doi.org/10.11113/jt.v78.8637

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211â€“252. https://doi.org/10.1007/s11263-015-0816-y

Schmidhuber, Ai, S., Dalle, I., & Galleria, S. (2014). Deep Learning in Neural Networks : An Overview. 1â€“88.

Vance, T. C., Merati, N., Yang, C., & Yuan, M. (2016). Cloud computing for ocean and atmospheric science. OCEANS 2016 MTS/IEEE Monterey, 1â€“4. https://doi.org/10.1109/OCEANS.2016.7761270

Yousif, N., Cole, J., Rothwell, J. C., Diedrichsen, J., Zelik, K. E., Winstein, C. J., Kay, D. B., Wijesinghe, R., Protti, D. A., Camp, A. J., Quinlan, E., Jacobs, J. V, Henry, S. M., Horak, F. B., Jacobs, J. V, Fraser, L. E., Mansfield, A., Harris, L. R.,

Merino, D. M., â€¦ Dublin, C. (2018). IMPLEMENTASI DEEP LEARNING UNTUK IMAGE CLASSIFICATION MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) PADA CITRA WAYANG GOLEK. Journal of Physical Therapy Science, 9(1), 1â€“11. http://dx.doi.org/10.1016/j.neuropsychologia.2015.07.010%0Ahttp://dx.doi.org/10.1016/j.visres.2014.07.001%0Ahttps://doi.org/10.1016/j.humov.2018.08.006%0Ahttp://www.ncbi.nlm.nih.gov/pubmed/24582474%0Ahttps://doi.org/10.1016/j.gaitpost.2018.12.007%0Ahttps:

DOI: https://doi.org/10.26760/mindjournal.v6i2.168-182