Impact of Contrast Limited Adaptive Histogram Equalization and Image Upscaling on Cataract Classification Using Deep Learning Models: Inception-ResNetV2, EfficientNetB0, and ResNet-50
DOI:
https://doi.org/10.52436/1.jutif.2026.7.3.5658Keywords:
Cataract Classification, CLAHE, Deep Learning, EfficientNetB0, Grad-CAM, Inception-ResNetV2, ResNet-50, UpscalingAbstract
Cataract is one of the leading causes of visual impairment worldwide, and its detection using retinal images remains a critical challenge in medical image analysis due to variations in image quality and subjectivity in clinical assessment. This study aims to evaluate the impact of image preprocessing techniques, namely Contrast Limited Adaptive Histogram Equalization (CLAHE) and image upscaling, on the performance and interpretability of deep learning–based cataract classification models. Three convolutional neural network architectures—Inception-ResNetV2, EfficientNetB0, and ResNet-50—were assessed using a balanced dataset of 2,000 retinal images under two experimental settings: raw images and enhanced images. The models were evaluated using accuracy, precision, recall, and F1-score, while Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to analyze model interpretability. Experimental results show that EfficientNetB0 achieved the highest accuracy on raw images (96%), followed by ResNet-50 (94%) and Inception-ResNetV2 (92%). After applying CLAHE and upscaling, ResNet-50 exhibited improved performance, reaching 95% accuracy, whereas EfficientNetB0 and InceptionResNetV2 experienced a decrease in accuracy to 83%. Grad-CAM visualizations indicate that all models consistently focused on clinically relevant regions associated with cataract characteristics. These findings demonstrate that image enhancement techniques do not universally improve classification performance and that their effectiveness is highly dependent on the underlying CNN architecture. The study provides practical insights for selecting appropriate preprocessing–model combinations to develop accurate, interpretable, and robust deep learning–based cataract classification systems for medical decision-support applications.
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P. A. Sulthon, Y. A. Romadhon, Moch. T. Azenta, and R. A. Zakaria, “Pencegahan Kebutaan melalui Edukasi dan Deteksi Dini Katarak di Desa Sanggung, Kabupaten Sukoharjo, Provinsi Jawa Tengah,” Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat, vol. 5, no. 1, pp. 19–28, May 2025, doi: 10.54082/jippm.751.
F. Damayanti et al., “Hubungan Diabetes Melitus Terhadap Penderita Katarak.” [Online]. Available: http://journal.scientic.id/index.php/sciena/issue/view/20
M. J. Greenberg and S. Bamba, “Diabetic cataracts,” Disease-a-Month, vol. 67, no. 5, May 2021, doi: 10.1016/j.disamonth.2021.101134.
I. D. Mienye, T. G. Swart, G. Obaido, M. Jordan, and P. Ilono, “Deep Convolutional Neural Networks in Medical Image Analysis: A Review,” Mar. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/info16030195.
S. Kumari and P. Singh, “Data efficient deep learning for medical image analysis: A survey,” Oct. 2023, [Online]. Available: http://arxiv.org/abs/2310.06557
P. K. Mall et al., “A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities,” Healthcare Analytics, vol. 4, Dec. 2023, doi: 10.1016/j.health.2023.100216.
T. Tuncer, P. D. Barua, I. Tuncer, S. Dogan, and U. R. Acharya, “A lightweight deep convolutional neural network model for skin cancer image classification,” Appl. Soft Comput., vol. 162, Sep. 2024, doi: 10.1016/j.asoc.2024.111794.
M. Alruwaili and M. Mohamed, “An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification,” Diagnostics, vol. 15, no. 5, Mar. 2025, doi: 10.3390/diagnostics15050551.
J. H. L. Goh et al., “Multi-Comparison of Different Ocular Imaging Modality-based Deep Learning Models for Visually Significant Cataract Detection,” Ophthalmology Science, vol. 5, no. 6, Nov. 2025, doi: 10.1016/j.xops.2025.100837.
C. J. Ejiyi et al., “Multi-modality medical image classification with ResoMergeNet for cataract, lung cancer, and breast cancer diagnosis,” Comput. Biol. Med., vol. 187, Mar. 2025, doi: 10.1016/j.compbiomed.2025.109791.
C. L. Lin and K. C. Wu, “Development of revised ResNet-50 for diabetic retinopathy detection,” BMC Bioinformatics, vol. 24, no. 1, Dec. 2023, doi: 10.1186/s12859-023-05293-1.
M. Hayati et al., “Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning,” in Procedia Computer Science, Elsevier B.V., 2022, pp. 57–66. doi: 10.1016/j.procs.2022.12.111.
M. K. Jalehi and B. M. Albaker, “Highly accurate multiclass classification of respiratory system diseases from chest radiography images using deep transfer learning technique,” Biomed. Signal Process. Control, vol. 84, Jul. 2023, doi: 10.1016/j.bspc.2023.104745.
J. Singh et al., “Identification of Brain Diseases using Image Classification: A Deep Learning Approach,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 186–192. doi: 10.1016/j.procs.2024.04.021.
M. M. M, M. T. R, V. K. V, and S. Guluwadi, “Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50,” BMC Med. Imaging, vol. 24, no. 1, Dec. 2024, doi: 10.1186/s12880-024-01292-7.
S. Srinivasan, D. Francis, S. K. Mathivanan, H. Rajadurai, B. D. Shivahare, and M. A. Shah, “A hybrid deep CNN model for brain tumor image multi-classification,” BMC Med. Imaging, vol. 24, no. 1, Dec. 2024, doi: 10.1186/s12880-024-01195-7.
M. Aamir, Z. Rahman, U. A. Bhatti, W. A. Abro, J. A. Bhutto, and Z. He, “An automated deep learning framework for brain tumor classification using MRI imagery,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-02209-2.
D. Muhammad and M. Bendechache, “Unveiling the black box: A systematic review of Explainable Artificial Intelligence in medical image analysis,” Dec. 01, 2024, Elsevier B.V. doi: 10.1016/j.csbj.2024.08.005.
I. Tuncer, S. Dogan, and T. Tuncer, “MobileDenseNeXt: Investigations on biomedical image classification,” Expert Syst. Appl., vol. 255, Dec. 2024, doi: 10.1016/j.eswa.2024.124685.
A. Sajust de Bergues de Escalup et al., “Deep learning-based image reconstruction significantly improves image quality of MRI examinations of the orbit at 3 Tesla,” Diagn. Interv. Imaging, 2025, doi: 10.1016/j.diii.2025.11.003.
E. Perdana et al., “KLASIFIKASI CITRA ULOS BATAK MENGGUNAKAN METODE SVM DAN KNN DENGAN EKSTRAKSI FITUR BERBASIS RESNET50,” 2025.
M. J. Alwazzan and A. M. Alattar, “Modified algorithm to enhance illumination and detail in colour retinal images using the CLAHE technique with a Wiener filter,” Biomed. Signal Process. Control, vol. 110, Dec. 2025, doi: 10.1016/j.bspc.2025.108241.
R. Santos, J. Pedrosa, A. M. Mendonça, and A. Campilho, “Grad-CAM: The impact of large receptive fields and other caveats,” Computer Vision and Image Understanding, vol. 258, Jul. 2025, doi: 10.1016/j.cviu.2025.104383.
D. Bhati, F. Neha, and M. Amiruzzaman, “A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging,” J. Imaging, vol. 10, no. 10, Oct. 2024, doi: 10.3390/jimaging10100239.
B. H. M. van der Velden, H. J. Kuijf, K. G. A. Gilhuijs, and M. A. Viergever, “Explainable artificial intelligence (XAI) in deep learning-based medical image analysis,” Jul. 01, 2022, Elsevier B.V. doi: 10.1016/j.media.2022.102470.
H. Sriraman, S. Badarudeen, S. Vats, and P. Balasubramanian, “A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics,” J. Multidiscip. Healthc., vol. 17, pp. 4411–4425, 2024, doi: 10.2147/JMDH.S446745.
T. N. Siregar and D. Juniati, “IMPLEMENTASI DIMENSI FRAKTAL BOX COUNTING DAN K-MEANS DALAM KLASIFIKASI JENIS PENYAKIT MATA BERDASARKAN CITRA FUNDUS RETINA.” [Online]. Available: https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k
B. J. Baladiah and A. Ikhssani, “LAPORAN KASUS : CANALICULITIS OD + KATARAK SENILE IMMATURE OS,” vol. 2, no. 3, 2021.
S. Muliani, B. Sukma Negara, M. Irsyad, and I. Iskandar, “Application of Shapley Additive Explanations (SHAP) in Deep Learning for Lung Disease Detection Using X-ray Images,” Journal of Artificial Intelligence and Software Engineering, vol. 5, no. 2, pp. 709–719, 2025, doi: 10.30811/jaise.v5i2.7044.
D. Clement Sumampouw et al., “OPTIMALISASI DETEKSI WAJAH DLIB-HOG PADA CITRA INTENSITAS RENDAH DENGAN PREPROCESSING CLAHE,” 2025.
Y. Yoshimi et al., “Image preprocessing with contrast-limited adaptive histogram equalization improves the segmentation performance of deep learning for the articular disk of the temporomandibular joint on magnetic resonance images,” Oral Surg. Oral Med. Oral Pathol. Oral Radiol., vol. 138, no. 1, pp. 128–141, Jul. 2024, doi: 10.1016/j.oooo.2023.01.016.
K. Wolk, J. Niklewski, M. Kopczynski, M. S. Tatara, and O. Zero, “Enhancing Semantic Forestry Segmentation Through Advanced Preprocessing With ML Models,” IEEE Access, vol. 13, pp. 98602–98621, 2025, doi: 10.1109/ACCESS.2025.3575713.
D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” 2021.
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