R and Python source code.

Tabular data is the predominant format for statistical analysis and machine learning across domains such as finance, biomedicine, and environmental sciences. However, conventional methods often face challenges when dealing with high dimensionality and complex nonlinear relationships. In contrast, deep learning models, particularly Convolutional Neural Networks (CNNs), are well-suited for automatic feature extraction and achieve high predictive accuracy, but are primarily designed for image-based inputs. This study presents a comparative evaluation of non-Euclidean distance metrics within the Image Generator for Tabular Data (IGTD) framework, which transforms tabular data into image representations for CNN-based classification. While the original IGTD relies on Euclidean distance, we extend the framework to adopt alternative metrics, including one minus correlation, Geodesic distance, Jensen-Shannon distance, Wasserstein distance, and Tropical distance. These metrics are designed to better capture complex, nonlinear relationships among features. Through systematic experiments on both simulated and real-world genomics datasets, we compare the performance of each distance metric in terms of classification accuracy and structural fidelity of the generated images. The results demonstrate that non-Euclidean metrics can significantly improve the effectiveness of CNN-based classification on tabular data. By enabling a more accurate encoding of feature relationships, this approach broadens the applicability of CNNs and offers a flexible, interpretable solution for high-dimensional, structured data across disciplines.