Perbandingan Teknik Prapemrosesan Citra terhadap Akurasi CNN dalam Deteksi Diabetic Retinopathy
DOI:
https://doi.org/10.67142/edutik.v6i2.341Keywords:
Diabetic Retinopathy, Convolutional Neural Network, Image PreprocessingAbstract
Diabetic Retinopathy (DR) is a diabetes complication and the leading cause of blindness in the working-age population, affecting more than 93 million people worldwide. The quality of retinal fundus images is significantly affected by lighting conditions and contrast, making image preprocessing a critical factor that influences the accuracy of deep learning-based detection models. This study aims to compare the effect of four preprocessing techniques—original images, color Histogram Equalization (HE), grayscale HE, and Color Constancy—on the performance of a Convolutional Neural Network (CNN) based on the AlexNet architecture for DR detection. The APTOS 2019 Kaggle dataset was used, comprising 3,722 color retinal fundus images: 1,830 non-DR and 1,892 DR images. Model validation was performed using 10-Fold Cross Validation, and performance was evaluated using confusion matrix, ROC curve, accuracy, sensitivity, and specificity. Results show that original images yielded the best overall performance with accuracy of 96.10%, sensitivity of 97.98%, specificity of 94.29%, and AUC of 0.994. Grayscale HE produced the highest AUC (0.996), while Color Constancy had the lowest AUC (0.989). These findings indicate that color information in fundus images contains important discriminative features, and preprocessing does not always improve overall accuracy. The AlexNet model shows potential for implementation as a DR screening system based on Computer-Aided Diagnosis (CAD) with relatively low computational complexity
References
Akhtar, S., Aftab, S., Ali, O., Ahmad, M., Khan, M. A., Abbas, S., & Ghazal, T. M. (2025). A deep learning based model for diabetic retinopathy grading. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-87171-9
Barath, D., & Kukelova, Z. (2019). Homography from two orientation- and scale-covariant features. http://arxiv.org/abs/1906.11927
Das, D., Biswas, S. K., & Bandyopadhyay, S. (2022). Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC). Multimedia Tools Appl., 82(19), 29943–30001. https://doi.org/10.1007/s11042-022-14165-4
Deva, J., Oktavian, N., Arishandy, Z. I., Pradana, A., Zakita, P., & Muttaqin, F. (2025). Perbandingan Model Transfer Learning Inceptionv3 dan Resnet50 untuk Klasifikasi Penyakit Mata Pada Citra Retina. In Jurnal Edutik: Pendidikan Teknologi Informasi dan Komunikasi (Vol. 5).
Dutta, S., Manideep, B. C. S., Basha, S. M., Caytiles, R. D., & Iyengar, N. C. S. N. (2018). Classification of diabetic retinopathy images by using deep learning models. International Journal of Grid and Distributed Computing, 11(1), 89–106. https://doi.org/10.14257/ijgdc.2018.11.1.09
Elkenawy, E.-S. M., Khodadadi, N., Gaber, K. S., Khodadadi, E., Alhussan, A. A., Khafaga, D. S., & Eid, M. M. (2026). Automated retinal disease classification using deep learning and AlexNet with statistical models analysis. PLOS ONE, 21(1), e0338415. https://doi.org/10.1371/journal.pone.0338415
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Fayyaz, A. M., Sharif, M. I., Azam, S., Karim, A., & El-Den, J. (2023). Analysis of Diabetic Retinopathy (DR) Based on the Deep Learning. Information (Switzerland), 14(1). https://doi.org/10.3390/info14010030
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (n.d.). ImageNet Classification with Deep Convolutional Neural Networks. Retrieved http://code.google.com/p/cuda-convnet/
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Momeni Pour, A., Seyedarabi, H., Abbasi Jahromi, S. H., & Javadzadeh, A. (2020). Automatic detection and monitoring of diabetic retinopathy using efficient convolutional neural networks and contrast limited adaptive histogram equalization. IEEE Access, 8, 136668–136673. https://doi.org/10.1109/ACCESS.2020.3005044
Muthusamy, D., & Palani, P. (2024). Deep learning model using classification for diabetic retinopathy detection: an overview. Artificial Intelligence Review, 57(7). https://doi.org/10.1007/s10462-024-10806-2
Narayanan, B. N., Hardie, R. C., De Silva, M. S., & Kueterman, N. K. (2020). Hybrid machine learning architecture for automated detection and grading of retinal images for diabetic retinopathy. Journal of Medical Imaging, 7(03), 1. https://doi.org/10.1117/1.jmi.7.3.034501
Shanthi, T., & Sabeenian, R. S. (2019). Modified Alexnet architecture for classification of diabetic retinopathy images. Computers & Electrical Engineering, 76, 56–64. https://doi.org/https://doi.org/10.1016/j.compeleceng.2019.03.004
Sun, H., Saeedi, P., Karuranga, S., Pinkepank, M., Ogurtsova, K., Duncan, B. B., Stein, C., Basit, A., Chan, J. C. N., Mbanya, J. C., Pavkov, M. E., Ramachandaran, A., Wild, S. H., James, S., Herman, W. H., Zhang, P., Bommer, C., Kuo, S., Boyko, E. J., & Magliano, D. J. (2022). IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Research and Clinical Practice, 183. https://doi.org/10.1016/j.diabres.2021.109119
Teo, Z. L., Tham, Y. C., Yu, M., Chee, M. L., Rim, T. H., Cheung, N., Bikbov, M. M., Wang, Y. X., Tang, Y., Lu, Y., Wong, I. Y., Ting, D. S. W., Tan, G. S. W., Jonas, J. B., Sabanayagam, C., Wong, T. Y., & Cheng, C. Y. (2021). Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis. Ophthalmology, 128(11), 1580–1591. https://doi.org/10.1016/j.ophtha.2021.04.027
Wan, S., Liang, Y., & Zhang, Y. (2018). Deep convolutional neural networks for diabetic retinopathy detection by image classification. Computers & Electrical Engineering, 72, 274–282. https://doi.org/https://doi.org/10.1016/j.compeleceng.2018.07.042
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Nurul Maulidiyah, I Gde Eka Dirgayussa, Rafli Filano, Yusuf Maulana (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
