DenseWave-OCT: A Hybrid Deep Learning and Wavelet Scattering Framework for Multi-Class Retinal Disease Classification in Optical Coherence Tomography Images

Optical Coherence Tomography (OCT) is a critical imaging modality for the early detection of retinal diseases; however, existing automated analysis approaches remain constrained by speckle noise, heterogeneous lesion morphologies, and subtle inter-class textural variations. This study introduces DenseWave-OCT, a hybrid classification framework that integrates deep semantic representations extracted from a pretrained DenseNet-121 backbone with multi-scale wavelet scattering coefficients designed to capture fine-grained structural and textural patterns. The two complementary feature sets are fused at the feature level into a unified descriptor, which is subsequently passed to a fully connected classifier. The proposed framework was evaluated on two publicly available benchmark datasets OCT2017 and Retinal OCT-C8 and benchmarked against established convolutional neural network baselines. DenseWave-OCT achieved classification accuracies of 99.48% and 98.29% on the two datasets, respectively, outperforming all evaluated baselines. These results demonstrate that combining deep convolutional features with wavelet-based textural representations yields a more discriminative and robust descriptor for OCT-based retinal disease classification, offering a promising direction for clinically reliable automated diagnostic tools.