<![CDATA[Deep learning models often falter when faced with small, imbalanced datasets or degraded image quality, leading to unacceptably high false prediction rates. To bridge this gap, we introduce class-based image composition. This technique reformulates training inputs by fusing multiple intra-class images into unique composite input images (CoImg). By concentrating information density and amplifying intra-class variance, CoImg forces the model to discern subtle, nuanced disease patterns that might otherwise be lost. We validated this approach using the optical coherence tomography dataset for image-based deep learning methods (OCTDL), a collection of seven imbalanced retinal disease scan categories. From this, we engineered Co-OCTDL: a perfectly balanced variant where each training sample exists as a 3×1 layout composite. To measure the impact of this new representation, we benchmarked the original dataset against its composite counterpart using a VGG16 architecture. Precision was paramount. We maintained identical hyperparameters and model structures across all experiments to ensure a rigorous, fair comparison. The results were transformative. While baseline datasets struggled, the enhanced Co OCTDL achieved a near-perfect F1-score of 0.995 and an AUC of 0.9996. The method effectively neutralized the risks of class imbalance. It didn't just improve the numbers; it refined the diagnostic reliability of the model.]]>
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