CATARACT CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN) INCEPTION RESNETV2

M. Mauludin Zulfa; Christian Sri Kusuma Aditya

doi:10.52436/1.jutif.2024.5.5.2340

M. Mauludin Zulfa Informatics, Engineering Faculty, University of Muhammadiyah Malang, Indonesia
Christian Sri Kusuma Aditya Informatics, Engineering Faculty, University of Muhammadiyah Malang, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.5.2340

Keywords: Cataract Classification, CNN, Inception-ResNetV2, Stohastic Gradient Descent (SGD)

Abstract

The eye is a human sensory device that functions as an organ of vision. Referring to data from the World Health Organization (WHO) in 2018, cataracts are responsible for 48% of blindness cases in the world and are the main cause in Indonesia. People still find it difficult to distinguish cataract eyes from normal eyes, so they often do not realize the indications of cataract disease. It is important to conduct early detection of cataract disease before blindness occurs. As technology develops, cataract identification becomes easier and simpler with digital image processing classification. This research develops a cataract image classification model using Convolutional Neural Network (CNN) with Inception-ResnetV2 architecture to identify cataract eyes with normal eyes. The proposed model consists of two parts of Inception-ResnetV2 architecture as the base model, and the head model in the form of Fully Connected Layers consisting of global average polling, 2 dense relu layers of 128 and 256 neurons, 2 batch normalization layers, 2 layers of dropout parameter 0.5, and softmax activation function for the output layer. To improve model training, the Stochastic Gradient Descent (SGD) optimization function is used. The dataset consists of 2,192 eye fundus images with 2 main classes of cataract and normal taken from the public data provider site Kaggle. Learning rate tests on the optimization function were carried out with parameters 0.1, 0.01, and 0.001, the results of the proposed model compiled with Stochastic Gradient Descent (SGD) learning rate 0.01 gave a final accuracy of 96%.

Downloads

Download data is not yet available.

References

M. Muafi, A. Wijaya, and V. A. Aziz, “Sistem Pakar Mendiagnosa Penyakit Mata Pada Manusia Menggunakan Metode Forward Chaining,” COREAI J. Kecerdasan Buatan, Komputasi dan Teknol. Inf., vol. 1, no. 1, pp. 43–49, 2020, doi: 10.33650/coreai.v1i1.1669.

L. D. B. Diki Hananta Firdaus, Bahtiar Imran and E. Suryadi, “Klasifikasi Penyakit Katarak Pada Mata Menggunakan Metode Convolutional Neural Network ( Cnn ) Berbasis Web Web-Based Classification of Cataract in the Eyes Using Convolutional Neural Network ( Cnn ) Method,” J. Kecerdasan Buatan dan Teknol. Inf., vol. 1, no. 3, pp. 18–26, 2022.

S. Manovita Pateda and P. Ayuningtias Mahdang, “Pendampingan Kader Kesehatan dalam Melakukan Deteksi Dini Penyakit Katarak dengan Metode ‘LIHAT,’” J. Kolaboratif Sains, vol. 6, no. 9, pp. 1133–1137, 2023, doi: 10.56338/jks.v6i9.4126.

P. Sisy Rizkia, “Jurnal Penelitian Perawat Profesional Pencegahan Tetanus,” Br. Med. J., vol. 2, no. 5474, pp. 1333–1336, 2020.

R. Dwi Hasriani, D. Pencegahan dan Pengendalian Penyakit Tidak Menular, and K. R. Kesehatan, “HIGEIA JOURNAL OF PUBLIC HEALTH RESEARCH AND DEVELOPMENT Hipertensi dengan Katarak pada Peserta Skrining Gangguan Penglihatan,” vol. 4, no. 4, pp. 645–655, 2020, [Online]. Available: http://journal.unnes.ac.id/sju/index.php/higeiahttps://doi.org/10.15294/higeia/v4i4/38745

Sisy Rizkia Putri, “Jurnal Penelitian Perawat Profesional Pencegahan Tetanus,” Br. Med. J., vol. 2, no. 5474, pp. 1333–1336, 2020.

R. A. Nurjanah, S. N. Indawaty, and M. Purwoko, “Faktor Risiko Timbulnya Low Vision Pasca Operasi Ekstrakapsular Avoidable tajam penglihatan hingga mencapai untuk Hasil,” Syifa`MEDIKA, vol. 10, no. 1, 2019.

Z. Farahdiva and A. Maisarah, “Screening Awal Penyakit Katarak dengan Image Processing menggunakan Metode Convolutional Neural Network pada Penyakit Akibat Kerja Pengelasan,” no. 2581, 2023.

Rendi Nurcahyo and Mohammad Iqbal, “Pengenalan Emosi Pembicara Menggunakan Convolutional Neural Networks,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 115–122, 2022, doi: 10.29207/resti.v6i1.3726.

V. B. Gianzurriell, F. Husnal, F. A. Wijaya, F. Fauzi, I. Paryudi, and I. Veritawati, “Analisis Gambar mri otak untuk mendeteksi tumor otak menggunakan algoritma cnn,” vol. 4, no. 2, pp. 14–18, 2023.

Bambang Pilu Hartato, “Penerapan Convolutional Neural Network pada Citra Rontgen Paru-Paru untuk Deteksi SARS-CoV-2,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 747–759, 2021, doi: 10.29207/resti.v5i4.3153.

D. Marcella, Y. Yohannes, and S. Devella, “Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network Dengan Arsitektur VGG-19,” J. Algoritm., vol. 3, no. 1, pp. 60–70, 2022, doi: 10.35957/algoritme.v3i1.3331.

E. G. Winarto and A. Lawi, “Implementasi Arsitektur Inception Resnet-V2 untuk Klasifikasi Kualitas Biji Kakao,” Konf. Nas. Ilmu Komput., pp. 132–137, 2021.

H. F. Jessar, A. T. Wibowo, and E. Rachmawati, “Klasifikasi Genus Tanaman Sukulen Menggunakan Convolutional Neural Network,” vol. 8, no. 2, pp. 3180–3196, 2021.

A. Ridhovan and A. Suharso, “Penerapan Metode Residual Network (Resnet) Dalam Klasifikasi Penyakit Pada Daun Gandum,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 7, no. 1, pp. 58–65, 2022, doi: 10.29100/jipi.v7i1.2410.

A. Satyo, B. Karno, D. Arif, I. Sari, and K. Wardhana, “Diagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet,” vol. 2, no. 1, pp. 57–66, 2021.

E. Andreas and W. Widhiarso, “Klasifikasi Penyakit Mata Katarak Menggunakan Convolutional Neural Network Dengan Arsitektur Inception V3,” MDP Student Conf., vol. 2, no. 1, pp. 107–113, 2023, doi: 10.35957/mdp-sc.v2i1.3660.

F. Masruroh, B. Surarso, and B. Warsito, “Perbandingan Kinerja Inception- Resnetv2, Xception, Inception-v3, dan Resnet50 pada Gambar Bentuk Wajah,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 1, pp. 11–20, 2023, doi: 10.25126/jtiik.20231014941.

L. Quaranta, F. Calefato, and F. Lanubile, “KGTorrent: A dataset of python jupyter notebooks from kaggle,” Proc. - 2021 IEEE/ACM 18th Int. Conf. Min. Softw. Repos. MSR 2021, pp. 550–554, 2021, doi: 10.1109/MSR52588.2021.00072.

C. A. Sugianto, “Analisis Komparasi Algoritma Klasifikasi Untuk Menangani Data Tidak Seimbang Pada Data Kebakaran Hutan,” Techno.Com, vol. 14, no. 4, pp. 336–342, 2019, [Online]. Available: http://publikasi.dinus.ac.id/index.php/technoc/article/view/992

B. J. Baladiah and A. Ikhsani, “Canaliculitis Od + Katarak Senile Immature Os : Laporan Kasus,” J. Kesehat. Tambusai, vol. 2, no. 3, pp. 199–203, 2021, doi: 10.31004/jkt.v2i3.2267.

T. N. Siregar and D. Juniati, “Implementasi Dimensi Fraktal Box Counting Dan K-Means dalam Klasifikasi Jenis Penyakit Mata Berdasarkan Citra Fundus Retina,” J. Ris. dan Apl. Mat., vol. 07, no. 02, pp. 206–223, 2023, [Online]. Available: https://journal.unesa.ac.id/index.php/jram/article/view/27730%0Ahttps://journal.unesa.ac.id/index.php/jram/article/view/27730/10595

J. O. Healthcare Engineering, “Retracted: On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer’s Disease Using Neuroimaging Modalities and Data Augmentation Methods,” J. Healthc. Eng., vol. 2023, p. 9769464, 2023, doi: 10.1155/2023/9769464.

R. Poojary, R. Raina, and A. K. Mondal, “Effect of data-augmentation on fine-tuned cnn model performance,” IAES Int. J. Artif. Intell., vol. 10, no. 1, pp. 84–92, 2021, doi: 10.11591/ijai.v10.i1.pp84-92.

Luqman Hakim, Z. Sari, and H. Handhajani, “Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 379–385, 2021, doi: 10.29207/resti.v5i2.3001.

R. Valentina, S. Rostianingsih, A. N. Tjondrowiguno, and J. S. Surabaya, “Pengenalan Gambar Botol Plastik dan Kaleng Minuman Menggunakan Metode Convolutional Neural Network,” J. Infra, vol. 8, no. 1, pp. 249–254, 2020.

A. Kholik, “Klasifikasi Menggunakan Convolutional Neural Network (Cnn) Pada Tangkapan Layar Halaman Instagram,” J. Data Min. dan Sist. Inf., vol. 2, no. 2, p. 10, 2021, doi: 10.33365/jdmsi.v2i2.1345.

A. Akram, K. Fayakun, and H. Ramza, “Klasifikasi Hama Serangga pada Pertanian Menggunakan Metode Convolutional Neural Network,” Build. Informatics, Technol. Sci., vol. 5, no. 2, pp. 397–406, 2023, doi: 10.47065/bits.v5i2.4063.

Ulfah Nur Oktaviana, Ricky Hendrawan, Alfian Dwi Khoirul Annas, and Galih Wasis Wicaksono, “Klasifikasi Penyakit Padi berdasarkan Citra Daun Menggunakan Model Terlatih Resnet101,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1216–1222, 2021, doi: 10.29207/resti.v5i6.3607.

Y. B. E. Purba, N. F. Saragih, A. P. Silalahi, and ..., “Perancangan Alat Pendeteksi Kematangan Buah Nanas Dengan Menggunakan Mikrokontroler Dengan Metode Convolutional Neural Network (CNN),” J. Ilm. Tek. …, vol. 2, no. 1, pp. 13–21, 2022, [Online]. Available: https://ojs.fikom-methodist.net/index.php/methotika/article/view/43%0Ahttps://ojs.fikom-methodist.net/index.php/methotika/article/download/43/39

D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform., vol. 5, no. 2, pp. 697–711, 2021.

N. D. Miranda, L. Novamizanti, S. Rizal, F. T. Elektro, and U. Telkom, “CONVOLUTIONAL NEURAL NETWORK PADA KLASIFIKASI SIDIK JARI MENGGUNAKAN RESNET-50 CLASSIFICATION OF FINGERPRINT PATTERN USING CONVOLUTIONAL NEURAL NETWORK IN CLAHE IMAGE,” vol. 1, no. 2, pp. 61–68, 2020.