Klasifikasi Penyakit Katarak Pada Mata Manusia Menggunakan Metode Convolutional Neural Network

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Elsyah Ayuningrum
Agus Eko Minarno
Galih Wasis Wicaksono

Abstract

Convolutional Neural Network (CNN) adalahsalah satu jenis neural network yang biasa digunakan pada data image. CNN bisa digunakan untuk mendeteksi dan mengenali objek pada sebuah image. Pre-trained CNN adalah suatu teknik atau metode memanfaatkan model yang sudah dilatih terhadap suatu dataet untuk menyelesaikan permasalahan lain yang serupa dengan cara menggunaakannya sebagai starting point, memodifikasi dan mengupdate parameternya sehingga sesuai dengan dataset yang baru. Klasifikasi dalam penelitian ini membangun dua model berbeda yaitu VGG16 dan ResNet50. Kedua model tersebut digunakan untuk klasifikasi gambar pada cataract dan normal. Dari penelitian yang dilakukan model yang mendapatkan nilai akurasi tertinggi yaitu VGG16.

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How to Cite
[1]
E. Ayuningrum, A. E. Minarno, and G. W. Wicaksono, “Klasifikasi Penyakit Katarak Pada Mata Manusia Menggunakan Metode Convolutional Neural Network”, JR, vol. 4, no. 4, Feb. 2024.
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References

S. Sourav, D. Bottari, I. Shareef, R. Kekunnaya, and B. Röder, “An electrophysiological biomarker for the classification of cataract-reversal patients: A case-control study,” EClinicalMedicine, vol. 27, pp. 1–11, 2020, doi: 10.1016/j.eclinm.2020.100559.

P. Kumari and K. R. Seeja, “Periocular Biometrics for non-ideal images: With off-the-shelf Deep CNN & Transfer Learning approach,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 344–352, 2020, doi: 10.1016/j.procs.2020.03.234.

V. Wirawan and Y. E. Soelistio, “Model Klasifikasi Mata Katarak dan Normal Menggunakan Histogram,” J. Ultim., vol. 9, no. 1, pp. 33–36, 2017, doi: 10.31937/ti.v9i1.561.

“Ocular Disease Recognition Using Convolutional Neural Networks | by Grzegorz Meller | Towards Data Science.” https://towardsdatascience.com/ocular-disease-recognition-using-convolutional-neural-networks-c04d63a7a2da (accessed Jun. 13, 2021).

A. U. Patwari, “Detection , Categorization , and Assessment of Eye Cataracts Using Digital Image Processing,” no. June, pp. 1–5, 2011.

S. Kolhe and S. K. Guru, “Remote Automated Cataract Detection System Based on Fundus Images,” pp. 10334–10341, 2016, doi: 10.15680/IJIRSET.2015.0506152.

J. Nayak, “Automated classification of normal, cataract and post cataract optical eye images using SVM classifier,” Lect. Notes Eng. Comput. Sci., vol. 1, no. c, pp. 542–545, 2013.

“Pengenalan Deep Learning Part 8 : Gender Classification using Pre-Trained Network (Transfer Learning) | by Samuel Sena | Medium.” https://medium.com/@samuelsena/pengenalan-deep-learning-part-8-gender-classification-using-pre-trained-network-transfer-37ac910500d1 (accessed Jun. 13, 2021).

“Pengenalan Deep Learning Part 7 : Convolutional Neural Network (CNN) | by Samuel Sena | Medium.” https://medium.com/@samuelsena/pengenalan-deep-learning-part-7-convolutional-neural-network-cnn-b003b477dc94 (accessed Jun. 13, 2021).

“Pengenalan Deep Learning Part 1 : Neural Network | by Samuel Sena | Medium.” https://medium.com/@samuelsena/pengenalan-deep-learning-8fbb7d8028ac (accessed Jun. 13, 2021).

“Top 4 Pre-Trained Models for Image Classification | With Python Code.” https://www.analyticsvidhya.com/blog/2020/08/top-4-pre-trained-models-for-image-classification-with-python-code/ (accessed Jun. 13, 2021).

S. Sourav et al., “Multi-channel Convolutions Neural Network Based Diabetic Retinopathy Detection from Fundus Images,” Procedia Comput. Sci., vol. 9, no. 1, pp. 37–45, 2019, doi: 10.1016/j.bspc.2020.102167.

“Ocular Disease Recognition | Kaggle.” https://www.kaggle.com/andrewmvd/ocular-disease-recognition-odir5k (accessed Jun. 13, 2021).

L. Perez and J. Wang, “The Effectiveness of Data Augmentation in Image Classification using Deep Learning,” 2017, [Online]. Available: http://arxiv.org/abs/1712.04621.