Analysis Comparison Classification Image Disease Eye Using the CNN Algorithm, Inception V3, DenseNet 121 and MobileNet V2 Architecture Models

Authors

  • Nasya Amirah Melyani Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
  • Ayuni Fachrunisa Lubis Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
  • Aghnia Tatamara Dokuz Eylül Üniversitesi, Turkey
  • Ryando Rama Haiban Yarmouk University, Jordan
  • Muhammad Iltizam Universiti Pendidikan Sultan Idris, Malaysia
  • Muhammad Aufi Rofiqi Al-Azhar University, Egypt
  • Sakhi Hasan Abdurrahman Al-Azhar University, Egypt
  • Nitasnim Samae Prince of Songkla University, Thailand
  • Bilal Shahid Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Pakistan
  • Muhammad Habibullah Al-Wasatiyah University, Yamen
  • Muhammad Ibrara Ismail Al-Wasatiyah University, Yamen

Keywords:

Cataract, Convolutional Neural Network, Diabetic Retinophaty, Eye Fundus, Glaukoma

Abstract

Eye disease is a significant global health problem, with more than two billion people experiencing vision impairment. Some of the main causes of visual impairment include cataracts, glaucoma, diabetic retinopathy, and age-related macular degeneration. Early detection of eye disease is very important to prevent blindness. The fundus of the eye, which includes the retina and blood vessels, is an important area in the diagnosis of retinal diseases. Fundus disease can cause significant vision loss and is one of the leading causes of blindness. Automated analysis of fundus images is used to diagnose common retinal diseases, ranging from easily treatable to very complex conditions. This research discusses eye disease image classification using several Convolutional Neural Network (CNN) architectures, namely Inception V3, DenseNet 121, and MobileNet V2. The dataset used is 4217 fundus images categorized based on the patient's health condition. Data is processed through normalization and augmentation to improve model performance. Experimental results show that MobileNet V2 has the highest accuracy of 81.3%, followed by Inception V3 with 77.3%, and DenseNet 121 with 76.7%. The use of appropriate CNN models in the classification of eye fundus images can help in early detection of eye diseases, thereby preventing further visual impairment.

References

M. C. S. Tang, S. S. Teoh, H. Ibrahim, and Z. Embong, “Neovascularization detection and localization in fundus images using deep learning,” Sensors, vol. 21, no. 16, Aug. 2021, doi: 10.3390/s21165327.

F. Du et al., “Recognition of eye diseases based on deep neural networks for transfer learning and improved D-S evidence theory,” BMC Med Imaging, vol. 24, no. 1, Dec. 2024, doi: 10.1186/s12880-023-01176-2.

A. E. Ilesanmi, T. Ilesanmi, and G. A. Gbotoso, “A systematic review of retinal fundus image segmentation and classification methods using convolutional neural networks,” Healthcare Analytics, vol. 4. Elsevier Inc., Dec. 01, 2023. doi: 10.1016/j.health.2023.100261.

H. Kaushik, D. Singh, M. Kaur, H. Alshazly, A. Zaguia, and H. Hamam, “Diabetic Retinopathy Diagnosis from Fundus Images Using Stacked Generalization of Deep Models,” IEEE Access, vol. 9, pp. 108276–108292, 2021, doi: 10.1109/ACCESS.2021.3101142.

Y. Pan et al., “Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases,” Front Physiol, vol. 14, 2023, doi: 10.3389/fphys.2023.1126780.

J. Wang, L. Yang, Z. Huo, W. He, and J. Luo, “Multi-Label Classification of Fundus Images with EfficientNet,” IEEE Access, vol. 8, pp. 212499–212508, 2020, doi: 10.1109/ACCESS.2020.3040275.

B. Bhatkalkar, A. Joshi, S. Prabhu, and S. Bhandary, “Automated fundus image quality assessment and segmentation of optic disc using convolutional neural networks,” International Journal of Electrical and Computer Engineering, vol. 10, no. 1, pp. 816–827, 2020, doi: 10.11591/ijece.v10i1.pp816-827.

M. Stanojevi?, D. Draškovi?, and B. Nikoli?, “Retinal disease classification based on optical coherence tomography images using convolutional neural networks,” J Electron Imaging, vol. 32, no. 03, Nov. 2022, doi: 10.1117/1.jei.32.3.032004.

A. O. Joshua, F. V Nelwamondo, and G. Mabuza-Hocquet, “Blood Vessel Segmentation from Fundus Images Using Modified U-net Convolutional Neural Network”, doi: 10.18178/joig.8.1.21-25.

V. Mayya, S. K. S, U. Kulkarni, D. K. Surya, and U. R. Acharya, “An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images,” Applied Intelligence, vol. 53, no. 2, pp. 1548–1566, Jan. 2023, doi: 10.1007/s10489-022-03490-8.

X. Yao et al., “Distribution of diabetic retinopathy in diabetes mellitus patients and its association rules with other eye diseases,” Sci Rep, vol. 11, no. 1, Dec. 2021, doi: 10.1038/s41598-021-96438-w.

N. Tsiknakis et al., “Deep learning for diabetic retinopathy detection and classification based on fundus images: A review,” Computers in Biology and Medicine, vol. 135. Elsevier Ltd, Aug. 01, 2021. doi: 10.1016/j.compbiomed.2021.104599.

G. Ali, A. Dastgir, M. W. Iqbal, M. Anwar, and M. Faheem, “A Hybrid Convolutional Neural Network Model for Automatic Diabetic Retinopathy Classification From Fundus Images,” IEEE J Transl Eng Health Med, vol. 11, pp. 341–350, 2023, doi: 10.1109/JTEHM.2023.3282104.

M. M. Butt, D. N. F. A. Iskandar, S. E. Abdelhamid, G. Latif, and R. Alghazo, “Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features,” Diagnostics, vol. 12, no. 7, Jul. 2022, doi: 10.3390/diagnostics12071607.

S. Duan et al., “Semi-supervised classification of fundus images combined with CNN and GCN,” J Appl Clin Med Phys, vol. 23, no. 12, Dec. 2022, doi: 10.1002/acm2.13746.

A. Skouta, A. Elmoufidi, S. Jai-Andaloussi, and O. Ouchetto, “Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural network,” J Big Data, vol. 9, no. 1, Dec. 2022, doi: 10.1186/s40537-022-00632-0.

M. S. Junayed, M. B. Islam, A. Sadeghzadeh, and S. Rahman, “CataractNet: An automated cataract detection system using deep learning for fundus images,” IEEE Access, vol. 9, pp. 128799–128808, 2021, doi: 10.1109/ACCESS.2021.3112938.

R. Sarki, K. Ahmed, H. Wang, Y. Zhang, J. Ma, and K. Wang, “Image Preprocessing in Classification and Identification of Diabetic Eye Diseases,” Data Sci Eng, vol. 6, no. 4, pp. 455–471, Dec. 2021, doi: 10.1007/s41019-021-00167-z.

R. Sarki, K. Ahmed, H. Wang, and Y. Zhang, “Automatic Detection of Diabetic Eye Disease through Deep Learning Using Fundus Images: A Survey,” IEEE Access, vol. 8. Institute of Electrical and Electronics Engineers Inc., pp. 151133–151149, 2020. doi: 10.1109/ACCESS.2020.3015258.

R. Mahum, S. U. Rehman, O. D. Okon, A. Alabrah, T. Meraj, and H. T. Rauf, “A novel hybrid approach based on deep cnn to detect glaucoma using fundus imaging,” Electronics (Switzerland), vol. 11, no. 1, Jan. 2022, doi: 10.3390/electronics11010026.

A. Neto, J. Camara, and A. Cunha, “Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device,” Sensors, vol. 22, no. 4, Feb. 2022, doi: 10.3390/s22041449.

H. N. Veena, A. Muruganandham, and T. Senthil Kumaran, “A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, pp. 6187–6198, Sep. 2022, doi: 10.1016/j.jksuci.2021.02.003.

M. J. M. Zedan, M. A. Zulkifley, A. A. Ibrahim, A. M. Moubark, N. A. M. Kamari, and S. R. Abdani, “Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review,” Diagnostics, vol. 13, no. 13. Multidisciplinary Digital Publishing Institute (MDPI), Jul. 01, 2023. doi: 10.3390/diagnostics13132180.

J. Latif, S. Tu, C. Xiao, S. Ur Rehman, A. Imran, and Y. Latif, “ODGNet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images,” SN Appl Sci, vol. 4, no. 4, Apr. 2022, doi: 10.1007/s42452-022-04984-3.

W. T. Song, I. C. Lai, and Y. Z. Su, “A Statistical Robust Glaucoma Detection Framework Combining Retinex, CNN, and DOE Using Fundus Images,” IEEE Access, vol. 9, pp. 103772–103783, 2021, doi: 10.1109/ACCESS.2021.3098032.

W. L. Alyoubi, M. F. Abulkhair, and W. M. Shalash, “Diabetic retinopathy fundus image classification and lesions localization system using deep learning,” Sensors, vol. 21, no. 11, Jun. 2021, doi: 10.3390/s21113704.

A. Saber, M. Sakr, O. M. Abo-Seida, A. Keshk, and H. Chen, “A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique,” IEEE Access, vol. 9, pp. 71194–71209, 2021, doi: 10.1109/ACCESS.2021.3079204.

Prof. N. P. Tembhare, Prof. P. U. Tembhare, and Prof. C. U. Chauhan, “Chest X-ray Analysis using Deep Learning,” Int J Res Appl Sci Eng Technol, vol. 11, no. 1, pp. 1441–1447, Jan. 2023, doi: 10.22214/ijraset.2023.48637.

A. A. Ali and F. A. A. Dawood, “Deep Learning of Diabetic Retinopathy Classification in Fundus Images,” Journal of Engineering, vol. 29, no. 12, pp. 139–152, Dec. 2023, doi: 10.31026/j.eng.2023.12.09.

A. Bin Tufail et al., “Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples,” Wirel Commun Mob Comput, vol. 2021, 2021, doi: 10.1155/2021/6013448.

P. D. Rinanda, D. N. Aini, T. A. Pertiwi, S. Suryani, and A. J. Prakash, “Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach,” Public Research Journal of Engineering, Data Technology and Computer Science, vol. 1, no. 2, pp. 56–61, Feb. 2024, doi: 10.57152/predatecs.v1i2.872.

A. Raj, N. A. Shah, A. K. Tiwari, and M. G. Martini, “Multivariate Regression-Based Convolutional Neural Network Model for Fundus Image Quality Assessment,” IEEE Access, vol. 8, pp. 57810–57821, 2020, doi: 10.1109/ACCESS.2020.2982588.

B. J. Bhatkalkar, D. R. Reddy, S. Prabhu, and S. V. Bhandary, “Improving the Performance of Convolutional Neural Network for the Segmentation of Optic Disc in Fundus Images Using Attention Gates and Conditional Random Fields,” IEEE Access, vol. 8, pp. 29299–29310, 2020, doi: 10.1109/ACCESS.2020.2972318.

A. O. Asia et al., “Detection of Diabetic Retinopathy in Retinal Fundus Images Using CNN Classification Models,” Electronics (Switzerland), vol. 11, no. 17, Sep. 2022, doi: 10.3390/electronics11172740.

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.

G. Bogacsovics, J. Toth, A. Hajdu, and B. Harangi, “Enhancing CNNs through the use of hand-crafted features in automated fundus image classification,” Biomed Signal Process Control, vol. 76, Jul. 2022, doi: 10.1016/j.bspc.2022.103685.

S. R. Shah, S. Qadri, H. Bibi, S. M. W. Shah, M. I. Sharif, and F. Marinello, “Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A Case Study on Early Detection of a Rice Disease,” Agronomy, vol. 13, no. 6, Jun. 2023, doi: 10.3390/agronomy13061633.

D. Hastari, S. Winanda, A. R. Pratama, N. Nurhaliza, and E. S. Ginting, “Application of Convolutional Neural Network ResNet-50 V2 on Image Classification of Rice Plant Disease,” Public Research Journal of Engineering, Data Technology and Computer Science, vol. 1, no. 2, Feb. 2024, doi: 10.57152/predatecs.v1i2.865.

A. M. Mutawa, S. Alnajdi, and S. Sruthi, “Transfer Learning for Diabetic Retinopathy Detection: A Study of Dataset Combination and Model Performance,” Applied Sciences (Switzerland), vol. 13, no. 9, May 2023, doi: 10.3390/app13095685.

Radhika KP and Vinay S, “Prediction of Diabetic Retinopathy Using InceptionV3 Model,” International Journal of Advances in Engineering and Management (IJAEM), vol. 4, p. 1327, 2022, doi: 10.35629/5252-040713271331.

S. Sheikh and U. Qidwai, “Using MobileNetV2 to Classify the Severity of Diabetic Retinopathy,” International Journal of Simulation Systems Science & Technology, Mar. 2020, doi: 10.5013/ijssst.a.21.02.16.

E. M. Dogo, O. J. Afolabi, and B. Twala, “On the Relative Impact of Optimizers on Convolutional Neural Networks with Varying Depth and Width for Image Classification,” Applied Sciences (Switzerland), vol. 12, no. 23, Dec. 2022, doi: 10.3390/app122311976.

D. Irfan and T. Surya Gunawan, “Comparison Of SGD, RMSProp, And Adam Optimation In Animal Classification Using CNNs,” 2nd International Conference on Information Science and Technology Innovation (ICoSTEC), pp. 1–7, 2023, doi: 10.35842/icostec.v2i.35.

C. Mohanty et al., “Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy,” Sensors, vol. 23, no. 12, Jun. 2023, doi: 10.3390/s23125726.

C. Guo, M. Yu, and J. Li, “Prediction of different eye diseases based on fundus photography via deep transfer learning,” J Clin Med, vol. 10, no. 23, Dec. 2021, doi: 10.3390/jcm10235481.

Y. Pan et al., “Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases,” Front Physiol, vol. 14, 2023, doi: 10.3389/fphys.2023.1126780.

D. Bhatt et al., “Cnn variants for computer vision: History, architecture, application, challenges and future scope,” Electronics (Switzerland), vol. 10, no. 20. MDPI, Oct. 01, 2021. doi: 10.3390/electronics10202470.

M. Abdullah, F. berhe Abrha, B. Kedir, and T. Tamirat Tagesse, “A Hybrid Deep Learning CNN model for COVID-19 detection from chest X-rays,” Heliyon, vol. 10, no. 5, Mar. 2024, doi: 10.1016/j.heliyon.2024.e26938.

Y. Pan et al., “Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases,” Front Physiol, vol. 14, 2023, doi: 10.3389/fphys.2023.1126780.

A. Souid, N. Sakli, and H. Sakli, “Classification and predictions of lung diseases from chest x? rays using mobilenet v2,” Applied Sciences (Switzerland), vol. 11, no. 6, Mar. 2021, doi: 10.3390/app11062751.

P. N. Srinivasu, J. G. Sivasai, M. F. Ijaz, A. K. Bhoi, W. Kim, and J. J. Kang, “Classification of skin disease using deep learning neural networks with mobilenet v2 and lstm,” Sensors, vol. 21, no. 8, Apr. 2021, doi: 10.3390/s21082852.

A. Taner, Y. B. Öztekin, and H. Duran, “Performance analysis of deep learning cnn models for variety classification in Hazelnut,” Sustainability (Switzerland), vol. 13, no. 12, Jun. 2021, doi: 10.3390/su13126527.

M. Stanojevi?, D. Draškovi?, and B. Nikoli?, “Retinal disease classification based on optical coherence tomography images using convolutional neural networks,” J Electron Imaging, vol. 32, no. 03, Nov. 2022, doi: 10.1117/1.jei.32.3.032004.

L. Ali, F. Alnajjar, H. Al Jassmi, M. Gochoo, W. Khan, and M. A. Serhani, “Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures,” Sensors, vol. 21, no. 5, pp. 1–22, Mar. 2021, doi: 10.3390/s21051688.

Published

2025-07-06

How to Cite

Melyani, N. A., Lubis, A. F., Tatamara, A., Haiban, R. R., Iltizam, M., Rofiqi, M. A., Abdurrahman, S. H., Samae, N., Shahid, B., Habibullah, M., & Ismail, M. I. (2025). Analysis Comparison Classification Image Disease Eye Using the CNN Algorithm, Inception V3, DenseNet 121 and MobileNet V2 Architecture Models. Public Research Journal of Engineering, Data Technology and Computer Science, 3(1). Retrieved from https://journal.irpi.or.id/index.php/predatecs/article/view/1559

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Vol. 3 No. 1: PREDATECS July 2025

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Articles

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Copyright (c) 2025 Nasya Amirah Melyani, Ayuni Fachrunisa Lubis, Aghnia Tatamara, Ryando Rama Haiban, Muhammad Iltizam, Muhammad Aufi Rofiqi, Sakhi Hasan Abdurrahman, Nitasnim Samae, Bilal Shahid, Muhammad Habibullah, Muhammad Ibrara Ismail

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Copyright © by Author; Published by Institut Riset dan Publikasi Indonesia (IRPI)

Creative Commons License

This Public Research Journal of Engineering, Data Technology and Computer Science is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.