Convolutional neural networks (CNNs) are widely recognized for their effectiveness in computer vision tasks, but their spatial information capturing ability does not directly apply to tabular datasets lacking spatial correlation. In this paper, a tailored approach called Expanded CNN (ExCNN) is proposed for tabular data analysis. Unlike common practices of transforming tabular data into images or using transformer architectures, ExCNN enhances feature dimensionality through a fully connected layer, harnessing the benefits of complex neural networks adapted to the tabular data domain. The performance of ExCNN is evaluated on various datasets, comparing it to existing architectures and benchmarking against Gradient Boosted Decision Trees. While no universally superior solution emerges, ExCNN demonstrates promise by leveraging the advantageous characteristics of CNNs for tabular data, outperforming certain deep learning architectures in specific metrics.