Srishti Raj; Anup Kumar Keshri

Building a Lightweight and Time-Efficient Hybrid Model for Choroiditis Detection in Fundus Images: A Systematic Approach

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Author(s)

Srishti Raj ^1,* Anup Kumar Keshri ¹

1. Computer Science & Engineering, BIT Mesra Campus, Birla Institute of Technology Mesra, Ranchi, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2026.04.01

Received: 22 Jul. 2025 / Revised: 19 Sep. 2025 / Accepted: 18 Dec. 2025 / Published: 8 Aug. 2026

Index Terms

Choroiditis, Hybrid Framework, MobileNet-V3-Small, Support Vector Machine, Transfer Learning, Fundus Image Classification

Abstract

While the use of artificial intelligence in ophthalmology has increased over the past few years, inflammatory diseases like choroiditis remain widely unexplored. A delayed diagnosis in such cases can cause severe complications that can lead to visual disability, and hence, automated diagnosis systems can be of great importance. In this study, we present a systematic, evidence-driven methodology for designing a lightweight choroiditis classifier using a real-world small dataset with a constraint of low resource availability. This hybrid framework consists of two components. The first feature extraction component extracts features using the MobileNet-V3-Small model. The second component, namely the classification component, utilises the Support Vector Machine as the binary classifier. This optimal combination was identified through systematic comparative experiments. Statistical testing confirms the robustness of the classifier selection. The model gives a cross-validation accuracy of 94% and a held-out test accuracy of 97.06% with a training time of approximately 3 minutes for the end-to-end pipeline on a carefully collected and previously introduced choroiditis dataset. Being lightweight and computationally efficient, this model is a suitable candidate for future development into a reliable computer-aided diagnosis tool that could assist experts in reviewing images, providing telemedicine care, and prioritising patient appointments based on the initial results of the automated systems.

Cite This Paper

Srishti Raj, Anup Kumar Keshri, "Building a Lightweight and Time-Efficient Hybrid Model for Choroiditis Detection in Fundus Images: A Systematic Approach", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.18, No.4, pp. 1-24, 2026. DOI:10.5815/ijigsp.2026.04.01

Reference

[1]U. Krishna, D. Ajanaku, A. K. Denniston, and T. Gkika, "Uveitis: a sight-threatening disease which can impact all systems," Postgrad. Med. J. 93, 766 (2017).
[2]C. E. Willoughby, D. Ponzin, S. Ferrari, A. Lobo, K. Landau, and Y. Omidi, "Anatomy and physiology of the human eye: effects of mucopolysaccharidoses disease on structure and function–a review," Clin. Exp. Ophthalmol. 38, 2 (2010).
[3]R. Geetha and K. Tripathy, "Chorioretinitis," in StatPearls [Internet], StatPearls Publishing, 2022.
[4]L. F. Nakayama et al., "Artificial intelligence in uveitis: A comprehensive review," Surv. Ophthalmol. 68, 669 (2023).
[5]J. J. González-López et al., "Development and validation of a Bayesian network for the differential diagnosis of anterior uveitis," Eye 30, 865 (2016).
[6]A. M. Mutawa and M. A. Alzuwawi, "Multilayered rule-based expert system for diagnosing uveitis," Artif. Intell. Med. 99, 101691 (2019).
[7]Y. Jamilloux et al., "Development and validation of a Bayesian network for supporting the etiological diagnosis of uveitis," J. Clin. Med. 10, 3398 (2021).
[8]I. Mellakh, T. Wang, R. Jacquot, N. Moalla, and R. Youssef-Douss, "Enhancing the Etiological Diagnosis of Uveitis with Artificial Intelligence: A Predictive Approach," 2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), 1 (2023).
[9]B. Trusko et al., "The standardization of uveitis nomenclature (SUN) project," Methods Inf. Med. 52, 259 (2013).
[10]A. A. Okada and D. A. Jabs, "The standardization of uveitis nomenclature project: the future is here," JAMA Ophthalmol. 131, 787 (2013).
[11]Y. Li, C. Lowder, X. Zhang, and D. Huang, "Anterior chamber cell grading by optical coherence tomography," Invest. Ophthalmol. Vis. Sci. 54, 258 (2013).
[12]S. Sharma et al., "Automated analysis of anterior chamber inflammation by spectral-domain optical coherence tomography," Ophthalmology 122, 1464 (2015).
[13]E. Baghdasaryan et al., "Analysis of ocular inflammation in anterior chamber—involving uveitis using swept-source anterior segment OCT," Int. Ophthalmol. 39, 1793 (2019).
[14]M. A. Sorkhabi et al., "Assessment of anterior uveitis through anterior-segment optical coherence tomography and artificial intelligence-based image analyses," Transl. Vis. Sci. Technol. 11, 7 (2022).
[15]C. L. Passaglia, T. Arvaneh, E. Greenberg, D. Richards, and B. Madow, "Automated method of grading vitreous haze in patients with uveitis for clinical trials," Transl. Vis. Sci. Technol. 7, 10 (2018).
[16]S. Haggag et al., "An automated CAD system for accurate grading of uveitis using optical coherence tomography images," Sensors 21, 5457 (2021).
[17]R. Parra et al., "A trust-based methodology to evaluate deep learning models for automatic diagnosis of ocular toxoplasmosis from fundus images," Diagnostics 11, 1951 (2021).
[18]R. Parra et al., "Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images with Residual Neural Networks," Stud. Health Technol. Inform. 281, 173 (2021).
[19]S. S. Alam, S. B. Shuvo, S. N. Ali, F. Ahmed, A. Chakma, and Y. M. Jang, "Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation," IEEE Access, vol. 12, pp. 22759–22775. (2024).
[20]J. Sun et al., "Identifying mouse autoimmune uveitis from fundus photographs using deep learning," Transl. Vis. Sci. Technol. 9, 59 (2020).
[21]S. Raj and A. K. Keshri, "Evaluation of machine learning classifiers for choroiditis prediction from fundus images," Grenze Int. J. Eng. Technol., vol. 9, no. 2, pp. 2269–2274, (2023).
[22]Scikit-learn developers, "User guide," Scikit-learn. [Online]. Available: https://scikit-learn.org/stable/user_guide.html. Accessed: Jan. 2024.
[23]A. Paszke et al., "PyTorch: An imperative style, high-performance deep learning library," Adv. Neural Inf. Process. Syst., vol. 32, 2019.
[24]S. Marcel and Y. Rodriguez, "Torchvision the machine-vision package of torch," Proc. 18th ACM Int. Conf. Multimedia, pp. 1485–1488, 2010.
[25]A. D. Forbes, "Classification-algorithm evaluation: Five performance measures based on confusion matrices," J. Clin. Monit. 11, 189 (1995).
[26]T. Fawcett, "An introduction to ROC analysis," Pattern Recognit. Lett. 27, 861 (2006).
[27]I. H. Sarker, "Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions," SN Comput. Sci. 2, 420 (2021).
[28]C. M. Bishop, "Pattern recognition and machine learning," Springer, 2, 645 (2006).
[29]T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, "The elements of statistical learning: data mining, inference, and prediction," Vol. 2, 1-758, Springer, New York (2009).
[30]T. Chen and C. Guestrin, "Xgboost: A scalable tree boosting system," Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 785 (2016).
[31]G. Ke et al., "Lightgbm: A highly efficient gradient boosting decision tree," Adv. Neural Inf. Process. Syst. 30 (2017).
[32]L. Prokhorenkova et al., "CatBoost: unbiased boosting with categorical features," Adv. Neural Inf. Process. Syst. 31 (2018).
[33]H. E. Kim et al., "Transfer learning for medical image classification: A literature review," BMC Med. Imaging 22, 69 (2022).
[34]K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 770 (2016).
[35]A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Adv. Neural Inf. Process. Syst. 25 (2012).
[36]K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556 (2014).
[37]F. N. Iandola et al., "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size," arXiv preprint arXiv:1602.07360 (2016).
[38]G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 4700 (2017).
[39]C. Szegedy et al., "Rethinking the inception architecture for computer vision," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2818 (2016).
[40]A. G. Howard et al., "Mobilenets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861 (2017).
[41]A. Howard et al., "Searching for mobilenetv3," Proc. IEEE/CVF Int. Conf. Comput. Vis., 1314 (2019).
[42]X. Zhang, X. Zhou, M. Lin, and J. Sun, "Shufflenet: An extremely efficient convolutional neural network for mobile devices," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 6848 (2018).
[43]M. Tan and Q. Le, "Efficientnet: Rethinking model scaling for convolutional neural networks," Int. Conf. Mach. Learn., 6105 (2019).
[44]C. Szegedy et al., "Going deeper with convolutions," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 1 (2015).
[45]T. G. Dietterich, "Approximate statistical tests for comparing supervised classification learning algorithms," Neural Comput., vol. 10, no. 7, pp. 1895–1923. (1998).
[46]L. van der Maaten and G. Hinton, "Visualizing Data using t-SNE," Journal of Machine Learning Research, vol. 9, pp. 2579–2605. (2008).
[47]C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn. 20, 273 (1995).
[48]N. Cristianini and J. Shawe-Taylor, "An introduction to support vector machines and other kernel-based learning methods," Cambridge University Press (2000).
[49]R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization," in Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 618–626. (2017).
[50]O. Cardozo et al., "Dataset of fundus images for the diagnosis of ocular toxoplasmosis," Data Brief, vol. 48, p. 109056. (2023).
[51]E. Pachetti and S. Colantonio, "A systematic review of few-shot learning in medical imaging," Artif. Intell. Med., vol. 156, p. 102949. (2024)

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