Reinventing CAD with a Composite AI Engine: Merging Capsule Routing Dynamics, Adaptive Reinforcement-Based Optimization, and Explainable Inference

Abstract

Despite widespread use of computer-aided diagnosis in imaging, clinical adoption is constrained by weak feature learning, static ensembling, and limited interpretability (Doi, 2007; Esteva et al., 2017; Giger & Suzuki, 2008; Kelly et al., 2019; Litjens et al., 2017; Topol, 2019; Wang & Summers, 2012). We introduce a ten-component framework that combines capsule routing for selective representation, multi-head attention for fusion, and a reinforcement agent that adaptively selects ensemble members (Cruz et al., 2018; Dietterich, 2000; Liu et al., 2019; Sabour et al., 2017; Sutton & Barto, 2018; Vaswani et al., 2017). Data scarcity is addressed through GAN-based augmentation, while integrated gradients, federated simulation, and Monte Carlo dropout jointly offer explainability, cross-site generalization, and uncertainty estimation (Begoli et al., 2019; Gal & Ghahramani, 2016; Holzinger et al., 2017; Kendall & Gal, 2017; Lundberg & Lee, 2017; Rudin, 2019; Shorten & Khoshgoftaar, 2019; Tjoa & Guan, 2020). Training stability is supported by cyclic scheduling and self-supervised pretraining. On synthetic skin lesion images, the model attains 88.44% accuracy, 93.33% F1-score, and 0.844 AUC. Ablation studies identify the capsule-ensemble combination as the main performance driver, while uncertainty quantification separately strengthens clinical trust (Ardila et al., 2019; Begoli et al., 2019; FDA, 2021; McKinney et al., 2020). These results demonstrate that accuracy, transparency, and deployment-ready robustness can be achieved together.