CleftNet v2: A Dual-Pipeline Deep Learning and Handcrafted-Feature Machine Learning Framework for Pre- and Post-Surgical Classification of Cleft Lip Images

The primary contribution of this paper is CleftNet v2, a dual-pipeline framework that resolves the clinical trade-off between high predictive accuracy and model transparency for pre- and post-surgical cleft lip image classification. The architecture pairs an end-to-end deep learning CNN (combining residual learning, multi-scale inception blocks, and squeeze-and-excitation channel attention) that achieves 93% test accuracy and 0.9776 ROC-AUC with an independent, highly interpretable machine learning ensemble. This second pipeline extracts a 305-dimensional vector of clinically motivated features—such as facial symmetry, quadrant-based tissue ratios, color, and texture—reaching up to 91.79% cross-validated accuracy while providing auxiliary outputs for severity, symmetry, age, and gender. Together, the dual approach delivers both top-tier automated diagnostic performance and transparent, human-readable metrics to support objective surgical auditing.