Transforming tabular data into images via enhanced spatial relationships for CNN processing.

ABSTRACT: Convolutional neural networks (CNNs), renowned for their efficiency in image analysis, have revolutionized pattern and structure recognition in visual data. Despite their success in image-based applications, CNNs face challenges when applied to tabular data due to the lack of inherent spatial relationships among features. This weakness can be overcome if the original tabular data is expanded to create an enhanced image that exhibits pseudo-spatial relationships. This paper introduces an original approach that transforms tabular data into a format suitable for CNN processing. The Novel Algorithm for Convolving Tabular Data (NCTD) applies mathematical transformations including rotation translation and reflection, to simulate spatial relationships within the data, thereby constructing a data

SUBMITTER: Alenizy HA 

PROVIDER: S-EPMC12084384 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature