Enhancing Retina Image Classification with a Hybrid ResNet-50 and Random Forest Model: A Comparative Study

Ankita Suryavanshi, Vinay Kukreja, Rajat Saini

2025

Abstract

Organs like the retina are diagnosed and managed using images and therefore, accurate classification of images is critical. This study presents a novel combined architecture that combines the training of ResNet-50 deep CNN with a Random Forest classifier to improve the ability to identify Moroccan retinal images. The proposed model leverages the strengths of both approaches: High-level features extracted by ResNet-50 for images and Random forest’s powerful classification. The paper has analyzed the performance of the proposed hybrid system based on a large set of data and established the positive effect of the new technique compared to the previous approaches. The model successfully had an accuracy of 94%. 3%, precision of 94. 0%, recall of 94. The corresponding precision is 89%, recall is 90% and an F1-score is 94%. 4%. Furthermore, the accuracy and loss for the training and validation set grows steadily for the epochs in the training and validation phase and the final validation set has an accuracy of 95%. 0%. These results have shown that in the field of retinal image classification, deep learning models particularly when used in combination with ensembles could yield the best performance. Of the advantages of the hybrid model, one is higher diagnostic accuracy in addition to the possible increase in the efficiency of the systems for the automated detection of pathologies in ophthalmology. Doing more work on the architecture of the model and expanding the deployment of the same for other medical imaging tasks could be future work used to improve on it.

Paper Citation

in Harvard Style

Suryavanshi A., Kukreja V. and Saini R. (2025). Enhancing Retina Image Classification with a Hybrid ResNet-50 and Random Forest Model: A Comparative Study. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 75-80. DOI: 10.5220/0013609100004664

in Bibtex Style

@conference{incoft25,
author={Ankita Suryavanshi and Vinay Kukreja and Rajat Saini},
title={Enhancing Retina Image Classification with a Hybrid ResNet-50 and Random Forest Model: A Comparative Study},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={75-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013609100004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Enhancing Retina Image Classification with a Hybrid ResNet-50 and Random Forest Model: A Comparative Study
SN - 978-989-758-763-4
AU - Suryavanshi A.
AU - Kukreja V.
AU - Saini R.
PY - 2025
SP - 75
EP - 80
DO - 10.5220/0013609100004664
PB - SciTePress