Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification.

The accurate diagnosis of retinal diseases, such as Diabetic Macular Edema (DME) and Age-related Macular Degeneration (AMD), is essential for preventing vision loss. Optical Coherence Tomography (OCT) imaging plays a crucial role in identifying these conditions, especially given the increasing prevalence of AMD. This study introduces a novel Reinforcement-Based Leveraging Transfer Learning (RBLTL) framework, which integrates reinforcement Q-learning with transfer learning using pre-trained models, including InceptionV3, DenseNet201, and InceptionResNetV2. The RBLTL framework dynamically optimizes hyperparameters, improving classification accuracy and generalization while mitigating overfitting. Experimental evaluations demonstrate remarkable performance, achieving testing accuracies of 98.75%, 98.90%, and 99.20% across three scenarios for multiclass OCT image classification. These results highlight the effectiveness of the RBLTL framework in categorizing OCT images for conditions like DME and AMD, establishing it as a reliable and versatile approach for automated medical image classification with significant implications for clinical diagnostics.