• Tidak ada hasil yang ditemukan

KLASIFIKASI DIABETIC RETINOPATHY MENGGUNAKAN ARSITEKTUR DEEP LEARNING MODEL CNN (CONVOLUTIONAL NEURAL NETWORK) - UTDI Repository

N/A
N/A
Protected

Academic year: 2024

Membagikan "KLASIFIKASI DIABETIC RETINOPATHY MENGGUNAKAN ARSITEKTUR DEEP LEARNING MODEL CNN (CONVOLUTIONAL NEURAL NETWORK) - UTDI Repository"

Copied!
3
0
0

Teks penuh

(1)

70

DAFTAR PUSTAKA

Alhudhaif, A., Polat, K. & Karaman, O., 2021, Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images, Expert Systems with Applications, 180.

Andriyani, W., Hartati, S., Wardoyo, R. & Wibowo, S., 2019, A Development of Modified Profile Matching and Borda for Determining Treatment Priorities for Hemorrhage Stroke Patients, , 10, 2.

Anonim, Journal of Medical Virology - 2020 - Sun - Understanding of COVID‐19 based on current evidence.pdf

Ashok, V., Hosmane, N., Mahagaonkar, G., Gudigar, A., & P, A., 2023, Diabetic Retinopathy Detection using Retinal Images and Deep Learning Model.

Ayalew, A.M., Salau, A.O., Abeje, B.T. & Enyew, B., 2022, Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients, Biomedical Signal Processing and Control, 74.

Gholamy, A., Kreinovich, V., & Kosheleva, O., 2-2018, Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation.

Gour, M. & Jain, S., 2022, Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network, Biocybernetics and Biomedical Engineering, 42, 1, 27–41.

Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., Xiao, Y., Gao, H., Guo, L., Xie, J., Wang, G., Jiang, R., Gao, Z., Jin, Q., Wang, J. & Cao, B., 2020, Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, The Lancet, 395, 10223, 497–506.

Islam, N., Ebrahimzadeh, S., Salameh, J.P., Kazi, S., Fabiano, N., … Scholefield, B., 2021, Thoracic imaging tests for the diagnosis of COVID-19, Cochrane Database of Systematic Reviews, 2021, 3.

Maria, Insana. Asuhan keperawatan diabetes mellitus dan asuhan keperawatan stroke. Deepublish, 2021.

O. Prof and Z. A. Hasibuan, “Deep Learning: Concept, Model, Algorithm, and Application,” 2020

Ohtera, Ryo, Takahiko Horiuchi, and Shoji Tominaga. "Eye-gaze detection from monocular camera image using parametric template matching." Computer Vision–ACCV 2007: 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part I 8. Springer Berlin Heidelberg, 2007.

J. W. G. Putra, “Pengenalan Konsep Pembelajaran Mesin dan Deep Learning,”

vol. 4, pp. 1–235, 2019, [Online]. Available:

http://www.researchgate.net/publication/323700644

J. Y. Choi, T. K. Yoo, J. G. Seo, J. Kwak, T. T. Um, and T. H. Rim, “Multi- categorical deep learning neural network to classify retinal images: A pilot study employing small database,” PLoS One, vol. 12, no. 11, pp. 1–16, 2017, doi: 10.1371/journal.pone.0187336.

Jabbar, M. K., Yan, J., Xu, H., Ur Rehman, Z., & Jabbar, A. (2022). Transfer

(2)

71

learning-based model for diabetic retinopathy diagnosis using retinal images. Brain Sciences, 12(5), 535.

Kalaivani, S. & Seetharaman, K., 2022, A three-stage ensemble boosted convolutional neural network for classification and analysis of COVID-19 chest x-ray images, International Journal of Cognitive Computing in Engineering, 3, 35–45.

López-Campos, J.L., Soler-Cataluña, J.J. & Miravitlles, M., 2020, Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2019 Report: Future Challenges, Archivos de Bronconeumologia, 56, 2, 65–67.

Luis, F. & Moncayo, G., No. Analisis Struktur Korespondensi Covariance terkait Indikator Kesehatan pada Lansia di Rumah dengan Fokus pada Persepsi Subjektif Kesehatan.

Majumder, S., & Kehtarnavaz, N. (2021), Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy.

Mahmud, T., Rahman, M.A. & Fattah, S.A., 2020, CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization, Computers in Biology and Medicine, 122.

Mikolajczyk, A., & Grochowski, M. (2018, May). Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW) (pp. 117-122). IEEE.

Musallam, A.S., Sherif, A.S. & Hussein, M.K., 2022, Efficient framework for detecting COVID-19 and pneumonia from chest X-ray using deep convolutional network, Egyptian Informatics Journal.

Peeri, N.C., Shrestha, N., Siddikur Rahman, M., Zaki, R., Tan, Z., Bibi, S., Baghbanzadeh, M., Aghamohammadi, N., Zhang, W. & Haque, U., 2021, The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?, International Journal of Epidemiology, 49, 3, 717–726.

Qiao, L., Zhu, Y., & Zhou, H. (2020). Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access, 8, 104292-104302.

Rajkumar, R. S., & Selvarani, A. G. (2022). Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering. Computer Systems Science & Engineering, 42(2).

Renith, G., & Senthilselvi, A. (2022). Analysis of diabetic retinopathy diagnosis using learning based algorithm. International Journal of Health Sciences, 6(S3), 419–430.

Skouta, A., Elmoufidi, A., Jai-Andaloussi, S., & Ouchetto, O. (2022).

Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network. Journal of Big Data, 9(1), 1-24.

Shorten, Connor & Khoshgoftaar, Taghi. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data. 6. 10.1186/s40537-

(3)

72

019-0197-0.

Tentang, P., Dan, I., Di, E.C.-, Labuhan, D. & Tahun, R., 2020, Jurnal Pengabdian Masyarakat Aufa ( JPMA) Volume 2 No.2 Agustus 2020, , 2, 2, 21–26.

Thanki, R. (2023). A deep neural network and machine learning approach for retinal fundus image classification. Healthcare Analytics, 3, 100140.

Vijayan, T., Sangeetha, M., Kumaravel, A., & Karthik, B. (2020). Fine tuned vgg19 convolutional neural network architecture for diabetic retinopathy diagnosis. Indian Journal of Computer Science and Engineering, 11(5), 615–

622.

Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., Zhao, Y., Li, Y., Wang, X. & Peng, Z., 2020, Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus- Infected Pneumonia in Wuhan, China, JAMA - Journal of the American Medical Association, 323, 11, 1061–1069.

Wei, J., Zhu, R., Zhang, H., Li, P., Okasha, A. & Muttar, A.K.H., 2021, Application of PET/CT image under convolutional neural network model in postoperative pneumonia virus infection monitoring of patients with non- small cell lung cancer, Results in Physics, 26.

Weyand, T., Kostrikov, I. & Philbin, J., 2016, Planet - photo geolocation with convolutional neural networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9912 LNCS, 37–55.

World Health Organization, 2020, Covid-19 Situation Report, World Health Organization, 31, 2, 61–66.

Yi, S. L., Yang, X. L., Wang, T. W., She, F. R., Xiong, X., & He, J. F. (2021).

Diabetic retinopathy diagnosis based on RA-EfficientNet. Applied Sciences, 11(22), 11035.

Referensi

Dokumen terkait

Kesimpulan yang dapat diambil berdasarkan hasil pengujian sistem pengenalan ekspresi wajah menggunakan Convolutional Neural Network adalah sebagai berikut : Metode

Pada Gambar 1 pengumpulan data dilakukan untuk mendapatkan data citra yang akan digunakan pada proses pelatihan dan pengujian model arsitektur Convolutional Neural

KEYWORD Deep Learning, Convolutional Neural Network, Klasifikasi Ekspresi Wajah, web flask KORESPONDENSI E-mail: Pulungadi1497@gmail.com PENDAHULUAN Ekspresi wajah adalah salah

Accelerating Convolutional Neural Network Training for Colon Histopathology Images by Customizing Deep Learning Framework * Abstract Methodology Result Conclusion & Future directions

Implementasi Deep Learning Menggunakan Metode Convolutional Neural Network Dalam Klasifikasi Gambar Warna Bola Pelampung.. Jurusan Teknik

Algoritma ini juga digunakan untuk mengambil data pola wajah, yang kemudian digunakan juga untuk proses mengenal wajah seseorang dengan metode convolutional neural network.. Algoritma

Dengan tujuan membangun sebuah model yang dapat mengklasifikasi daun the siap panen menggunakan metode Convolutional Neural Network dengan arsitektur MobileNetV2 sehingga dapat membantu

In this study we target the classification of Alzheimer's disease images using convolutional neural network CNN and transfer learning VGG16 and VGG19.. The objective of this study is