This paper introduces a novel method to improve classification performance and interpretability in single-cell omics analysis by transforming high-dimensional data into structured images. Single-cell omics provides insight into molecular behavior at the cellular level, but datasets are often complex, noisy, and difficult to analyze with traditional techniques. Existing approaches usually represent cells as unstructured vectors, overlooking gene interactions and limiting accuracy. We propose an omics-to-image pipeline that converts high-dimensional data into spatially consistent visual representations for deep learning. Using model-aware feature ranking, K-means clustering, and RGB channel encoding, functionally related genes are grouped and mapped into images, which are then used to train convolutional neural networks (CNNs) for cell-type classification. Experiments on real-world single-cell datasets show that our RGB-based approach outperforms baselines such as grayscale encoding and random gene placement, improving accuracy, training stability, and interpretability by highlighting features most critical to predictions using Grad-CAM. By integrating computer vision and biological knowledge, this project offers a practical, interpretable framework for single-cell omics analysis with potential applications in disease classification, diagnosis, and personalized medicine.