Machine Unlearning Based Pre and Post Surgery Classification of Cleft Lip and Nasal Images

The core research contribution of this work is the development of a privacy-preserving medical image classification framework that integrates a custom CleftNet v2 deep learning architecture with SISA-based Machine Unlearning and Grad-CAM explainability. Designed for classifying pre-operative and post-operative cleft lip facial images, the framework combines Inception modules, residual connections, and squeeze-and-excitation attention blocks to extract multi-scale spatial features, achieving an overall classification accuracy of 93.36% and an ROC-AUC of 97.76%. Crucially, by incorporating the SISA (Sharded, Isolated, Sliced, and Aggregated) paradigm alongside a complementary 305-dimensional handcrafted feature extraction pipeline, the system enables the selective, rapid removal of specific patient data upon request through isolated shard retraining. This eliminates the need for full model retraining while supporting clinical interpretability and compliance with stringent data privacy regulations like GDPR’s “Right to Be Forgotten”.