CutisAI: Deep Learning Framework for Automated Dermatology and Cancer Screening
Rohit Kaushik, Eva Kaushik
TL;DR
CBDC tackles dermatology AI reliability by integrating three theoretical pillars: Statistical Learning Theory for distribution-aware generalization, Topological Data Analysis for stable morphological embeddings, and Bayesian–Conformal Inference for finite-sample calibrated uncertainty. The architecture fuses CNN and Transformer feature learning with a differentiable topological module and a multi-term loss to yield discriminative, robust, and interpretable predictions. On HAM10000, PH2, and ISIC-2020, CBDC achieves high accuracy with well-calibrated uncertainty, providing clinically meaningful risk scores and prediction intervals. The work offers a principled pathway toward deployable, trustworthy dermatological AI in hospitals, tele-dermatology, and resource-limited settings, with provable guarantees such as $\mathcal{R}(f_\theta) - \hat{\mathcal{R}}(f_\theta) \le L^2 \mathfrak{R}_N(\mathcal{F}) + \sqrt{\frac{\log(1/\delta)}{2N}}$ and $ \Pr[y^* \in \Gamma_\alpha(x^*)] \ge 1-\alpha$.
Abstract
The rapid growth of dermatological imaging and mobile diagnostic tools calls for systems that not only demonstrate empirical performance but also provide strong theoretical guarantees. Deep learning models have shown high predictive accuracy; however, they are often criticized for lacking well, calibrated uncertainty estimates without which these models are hardly deployable in a clinical setting. To this end, we present the Conformal Bayesian Dermatological Classifier (CBDC), a well, founded framework that combines Statistical Learning Theory, Topological Data Analysis (TDA), and Bayesian Conformal Inference. CBDC offers distribution, dependent generalization bounds that reflect dermatological variability, proves a topological stability theorem that guarantees the invariance of convolutional neural network embeddings under photometric and morphological perturbations and provides finite conformal coverage guarantees for trustworthy uncertainty quantification. Through exhaustive experiments on the HAM10000, PH2, and ISIC 2020 datasets, we show that CBDC not only attains classification accuracy but also generates calibrated predictions that are interpretable from a clinical perspective. This research constitutes a theoretical and practical leap for deep dermatological diagnostics, thereby opening the machine learning theory clinical applicability interface.