Output layer configurations for binary classification: single-neuron versus dual-neuron approaches

Binary classification is a fundamental machine learning problem with applications spanning image recognition, medical diagnosis, and natural language processing. Neural networks typically use either a single output neuron with sigmoid activation or two output neurons with softmax activation to model class probabilities. However, practical guidance on choosing between these output configurations remains unclear in many real-world scenarios. This study provides a comprehensive empirical comparison of these two output layer configurations, investigating their influence on model performance, training dynamics, and generalization capabilities. Despite the theoretical equivalence of single-neuron and dual-neuron output layers for binary classification, there is a notable lack of systematic empirical studies comparing their impact on model performance, convergence, and generalization. This absence of clear, data-driven guidance poses challenges for practitioners, especially as neural network architectures grow more complex. Through extensive experimentation on diverse neural network architectures—including a custom Small Convolutional Neural Network (CNN), Vision Transformer (ViT), and ResNet-50—applied to benchmark datasets derived from CIFAR-10, we identify conditions under which each approach shows superior characteristics. Both output layer configurations were implemented across all architectures and trained under identical protocols. In these standard implementations, single-neuron models use binary cross-entropy (BCE) and dual-neuron models use categorical cross-entropy (CCE); therefore, observed differences reflect this paired design. Our findings are based on five-seed repeats per setting (mean ± std) and per-task paired statistical tests. The single-neuron output with sigmoid activation and BCE loss shows a statistically significant advantage for the Small CNN on an easier task, while modern architectures (ViT, ResNet-50) show comparable performance with small, non-significant trends, with absolute gaps typically <0.3%. Output-layer choice has negligible impact on computational efficiency (floating-point operations (FLOPs), inference latency, and throughput), and parameter overhead remains negligible (<0.1%). Convergence differences are generally modest and are interpreted descriptively in this study. These results provide descriptive empirical evidence for CIFAR-10–derived binary image tasks. They suggest that the standard single-neuron output with BCE loss is a reasonable default in our setting. Broader or causal generalizations would require loss-controlled ablations and wider datasets.