Disparitas Efektivitas CLAHE pada Berbagai Arsitektur Deep Learning untuk Klasifikasi Katarak Berbasis Citra Fundus
- Frida Ramadhani Universitas Dian Nuswantoro, Semarang, Indonesia
-
Cinantya Paramita
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
- Egia Rosi Subhiyakto Universitas Dian Nuswantoro, Semarang, Indonesia (*) Corresponding Author
Abstract
This study aims to highlight and compare the performance of three deep learning architectures, namely CNN, VGG16, and EfficientNet-B1, in classifying cataract conditions based on retinal fundus images. A total of 2600 fundus images of two classes (normal and cataract) were collected from open sources and processed in two versions: the original images and contrast-enhanced images using Contrast Limited Adaptive Histogram Equalization (CLAHE). Each model was tested using both versions of the dataset, with evaluation based on accuracy, precision, recall, and F1 score. The results of this experiment show that the application of CLAHE is proven to improve the accuracy of CNN from 0.89 (89%) to 0.97 (97%) and, importantly for clinical diagnosis, improve the recall for cataract class from 0.81 (81%) to 0.97 (97%) with precision 0.98 (98%), f1 score 0.97 (97%) and reduce the number of False Negatives (FN) from 9 to 6. Similarly, it improves the accuracy of VGG16 from 0.93 (93%) (with precision 0.91 (91%), recall 0.96 (96%), f1 score 0.94 (94%)) to 0.96 (96%) (precision 0.94 (94%), recall 0.98 (98%), f1 score 0.96 (96%), and also reduces the number of FN from 9 to 6, thereby improving clinical reliability. In contrast to the EfficientNet-B1 Model, CLAHE does not provide significant improvement. significant. significant, with an accuracy of 0.97 (97%), precision of 0.98 (98%), recall of 0.98 (98%), and f1 score of 0.97 (97%), the accuracy performance actually decreased to 0.96 (96%) and precision to 0.94 (94%). This shows that the effectiveness of preprocessing techniques is highly dependent on the model architecture used. CLAHE has been shown to be effective on conventional models such as CNN and VGG16, but is less optimal for complex pretrained models such as EfficientNet-B1. These findings contribute to the development of adaptive and efficient medical image classification systems, particularly in the context of automated cataract screening in primary healthcare.
Downloads
References
World Health Organization, “Blindness and vision impairment,” World Health Organization. Accessed: Nov. 09, 2025. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment
Badan Penelitian dan Pengembangan Kesehatan, “Hasil Utama Riskesdas 2018,” Jakarta, 2018. Accessed: Nov. 09, 2025. [Online]. Available: https://www.litbang.kemkes.go.id/laporan-riset-kesehatan-dasar-riskesdas-2018/
R. RAHMADWATI, A. Z. IMRAN, M. ASWIN, and K. FERDIANA, “Identifikasi Penyakit Katarak berdasarkan Citra Fundus menggunakan Siamese Convolutional Neural Network,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 12, no. 4, p. 838, Dec. 2024, doi: 10.26760/elkomika.v12i4.838.
C. Paramita, S. Rakasiwi, P. N. Andono, G. F. Shidik, Shier Nee Saw, and M. I. Rafsanjani, “Deep Learning-Based Eye Disorder Classification: A K-Fold Evaluation of EfficientNetB and VGG16 Models,” Scientific Journal of Informatics, vol. 12, no. 3, pp. 441–452, Sep. 2025, doi: 10.15294/sji.v12i3.26257.
C. A. Putri and S. Rakasiwi, “Diagnosis Dini Penyakit Mata: Klasifikasi Citra Fundus Retina dengan Convolutional Neural Network VGG-16,” Edumatic: Jurnal Pendidikan Informatika, vol. 9, no. 1, pp. 208–216, Apr. 2025, doi: 10.29408/edumatic.v9i1.29571.
A. Azmiardi et al., “Evaluating the effectiveness of the indonesian diabetes self-management questionnaire in managing type 2 diabetes in primary care,” Jurnal Kedokteran dan Kesehatan, vol. 16, no. 1, pp. 28–34, 2025, doi: 10.30659/sainsmed.v16i1.39770.
T. Abraham, Todd A., D. A. Orringer, and R. Levenson, “Applications of artificial intelligence for image enhancement in pathology,” S. Cohen, Ed., Elsevier, 2021, pp. 119–148.
Noor F., Dinur Syahputra D., Muhathir M., and Fadlisyah F., “Analysis of Combined Contrast Limited Adaptive Histogram Equalization (CLAHE) and Median Filter Methods for Enhancement of CCTV Screenshot Image Quality,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 8, no. 2, 2025, doi: 10.31289/jite.v8i2.14016.
M. J. Alwazzan, M. A. Ismael, and A. N. Ahmed, “A Hybrid Algorithm to Enhance Colour Retinal Fundus Images Using a Wiener Filter and CLAHE,” J Digit Imaging, vol. 34, no. 3, pp. 750–759, Jun. 2021, doi: 10.1007/s10278-021-00447-0.
P. Hernández-Cámara, J. Vila-Tomás, P. Dauden-Oliver, N. Alabau-Bosque, V. Laparra, and J. Malo, “Why Divisive Normalization works in image segmentation?,” Neurocomputing, vol. 649, p. 130569, Oct. 2025, doi: 10.1016/j.neucom.2025.130569.
H. Tarwani, S. Patel, and P. Goel, “Deep learning approach for weather classification using pre-trained convolutional neural networks,” Procedia Comput Sci, vol. 252, pp. 136–145, 2025, doi: 10.1016/j.procs.2024.12.015.
J. Deepa and P. Madhavan, “An advanced skin lesion segmentation and classification framework using deep learning strategies,” Sci Rep, vol. 15, no. 1, p. 33926, Sep. 2025, doi: 10.1038/s41598-025-08255-0.
F. A. Mohammed, K. K. Tune, B. G. Assefa, M. Jett, and S. Muhie, “Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature,” Mach Learn Knowl Extr, vol. 6, no. 1, pp. 699–736, Mar. 2024, doi: 10.3390/make6010033.
M. Opoku, B. A. Weyori, A. F. Adekoya, and K. Adu, “CLAHE-CapsNet: Efficient retina optical coherence tomography classification using capsule networks with contrast limited adaptive histogram equalization,” PLoS One, vol. 18, no. 11, p. e0288663, Nov. 2023, doi: 10.1371/journal.pone.0288663.
L. Schneider, A. Krasowski, V. Pitchika, L. Bombeck, F. Schwendicke, and M. Büttner, “Assessment of CNNs, transformers, and hybrid architectures in dental image segmentation,” J Dent, vol. 156, p. 105668, May 2025, doi: 10.1016/j.jdent.2025.105668.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Apr. 2015.
A. Arora, S. Gupta, S. Singh, and J. Dubey, “Eye Disease Detection Using Transfer Learning on VGG16,” 2023, pp. 527–536. doi: 10.1007/978-981-19-1142-2_42.
S. Nigam, R. Jain, V. K. Singh, S. Marwaha, A. Arora, and S. Jain, “EfficientNet architecture and attention mechanism-based wheat disease identification model,” Procedia Comput Sci, vol. 235, pp. 383–393, 2024, doi: 10.1016/j.procs.2024.04.038.
Mohamed Musthafa M, Mahesh T. R, Vinoth Kumar V, and Suresh Guluwadi, “Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50,” BMC Med Imaging, vol. 24, no. 1, p. 107, May 2024, doi: 10.1186/s12880-024-01292-7.
L. R. M. Lanzafame et al., “Deep Learning Denoising Algorithm for Improved Assessment of Coronary Arteries in Transcatheter Aortic Valve Implantation CT Imaging,” Acad Radiol, Nov. 2025, doi: 10.1016/j.acra.2025.10.030.
A. Sharma, A. Dogra, B. Goyal, A. Saini, and V. Kukreja, “From Spatial Domain to Patch-Based Models: A Comprehensive Review and Comparison of Multimodal Medical Image Denoising Algorithms,” 2025, Tech Science Press. doi: 10.32604/cmc.2025.066481.
D. Li and S. Li, “An artificial intelligence deep learning platform achieves high diagnostic accuracy for Covid-19 pneumonia by reading chest X-ray images,” iScience, vol. 25, no. 4, Apr. 2022, doi: 10.1016/j.isci.2022.104031.
M. A. Widyananda, C. Paramita, C. Supriyanto, A. W. Wibowo, D. W. Utomo, and S. T. Widyaatmadja, “YOLOVX Method for Cataract Early Detection,” in 2025 International Conference on Smart Computing, IoT and Machine Learning, SIML 2025, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/SIML65326.2025.11080840.
C. Paramita, C. Supriyanto, P. Šolić, C. Wada, and A. A. Dzaky, “Performance Evaluation of YOLOv8 Models for Multi-Class Skin Lesion Detection from Dermoscopic Images,” in 2025 International Conference on Smart Computing, IoT and Machine Learning, SIML 2025, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/SIML65326.2025.11080819.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Disparitas Efektivitas CLAHE pada Berbagai Arsitektur Deep Learning untuk Klasifikasi Katarak Berbasis Citra Fundus
Pages: 1785-1796
Copyright (c) 2025 Frida Ramadhani, Cinantya Paramita, Egia Rosi Subhiyakto
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
