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. 2025 Feb 18;15(1):5888.
doi: 10.1038/s41598-025-89961-7.

HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification

Shivani Agarwal  1 , Anand Kumar Dohare  2 , Pranshu Saxena  3 , Jagendra Singh  3 , Indrasen Singh  4 , Umesh Kumar Sahu  5
Affiliations

HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification

Shivani Agarwal et al. Sci Rep. .

Erratum in

Abstract

Optical Coherence Tomography (OCT) plays a crucial role in diagnosing ocular diseases, yet conventional CNN-based models face limitations such as high computational overhead, noise sensitivity, and data imbalance. This paper introduces HDL-ACO, a novel Hybrid Deep Learning (HDL) framework that integrates Convolutional Neural Networks with Ant Colony Optimization (ACO) to enhance classification accuracy and computational efficiency. The proposed methodology involves pre-processing the OCT dataset using discrete wavelet transform and ACO-optimized augmentation, followed by multiscale patch embedding to generate image patches of varying sizes. The hybrid deep learning model leverages ACO-based hyperparameter optimization to enhance feature selection and training efficiency. Furthermore, a Transformer-based feature extraction module integrates content-aware embeddings, multi-head self-attention, and feedforward neural networks to improve classification performance. Experimental results demonstrate that HDL-ACO outperforms state-of-the-art models, including ResNet-50, VGG-16, and XGBoost, achieving 95% training accuracy and 93% validation accuracy. The proposed framework offers a scalable, resource-efficient solution for real-time clinical OCT image classification.

Keywords: Ant colony optimization; Data Imbalance; Hybrid deep learning; Hyperparameter tuning; OCT image classification; Optical coherence tomography.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethical approval: Ethical and professional standards have been met. No animals or humans are used in this study.

Figures

Fig. 1
Fig. 1
Graphical abstract of proposed deep learning architecture.
Fig. 2
Fig. 2
Modified ViT architecture.
Fig. 3
Fig. 3
Number of image before and after resampling.
Fig. 4
Fig. 4
Accuracy graph among State-of-art-techniques (a) training graph (b) validation graph.
Fig. 5
Fig. 5
ACO Convergence graph between fitness vs. Iteration count.
See this image and copyright information in PMC

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