This work addresses the problem of automatic image classification, focusing on its application to historical press digitization. Specifically, it proposes a method for automatic image discrimination to detect issues such as skew, noise, curvature, and the combination of several previous problems. Empirically, we have observed that addressing some of these image problems increases the quality of optical character recognition and segmentation of newspaper columns. Therefore, it is necessary to predict the problem of a newspaper page and treat it appropriately within the workflow. For this purpose, the problem has been studied by evaluating different pre-trained Convolutional Neural Networks for computer vision problems, such as RestNet variants, AlexNet, VGG11, and EfficientNet. The main contribution of this work is the development of an algorithm for threshold estimation in deep neural networks using metaheuristic optimization, specifically designed for multi-label classification tasks. We employ Particle Swarm Optimization and Genetic Algorithm as representative metaheuristic approaches for optimizing decision thresholds. In addition, we introduce a novel dataset of historical press images, collaboratively annotated by human experts. As a baseline, we propose a Random Search strategy for threshold selection, which is compared against the metaheuristic-based methods. The paper outlines the full computational methodology, including problem formulation, neural architecture design, and dataset construction, and presents experimental results across various deep learning models. The proposed approach achieves consistent improvements in F1-score relative to both the naive thresholding strategy and the baseline method.