The primary objective of this paper was to present an original neural network architecture specifically tailored for transforming electrical measurement data into images in industrial tomography applications. To achieve this goal, a multi-branch differential neural network was developed, featuring specialized “negation” layers capable of explicitly computing differences between parallel branches. These layers enable the network to emphasize subtle variations within input data by effectively subtracting one set of learned features from another. The proposed differential neural architecture was comprehensively evaluated and benchmarked against four established methodologies, encompassing both deterministic approaches (Tikhonov regularization and Total Variation method) and machine learning-based solutions (Elastic Net regression and standard Long Short-Term Memory networks). Results clearly demonstrated the superiority of the proposed differential model in terms of accuracy, robustness to noise, and overall image re-construction quality. The differential approach not only exhibited improved performance metrics but also provided significant advantages in handling common-mode artifacts prevalent in industrial tomography measurements. Consequently, this study confirms the potential of differential neural networks with integrated negation operations as powerful tools in industrial tomography, offering enhanced capabilities for anomaly detection, artifact suppression, and precise image reconstruction.