CutisAI: Deep Learning Framework for Automated Dermatology and Cancer Screening

TL;DR

This paper introduces CBDC, a deep learning framework for dermatology that combines theoretical guarantees with practical accuracy and calibrated uncertainty estimates, enhancing clinical reliability.

Contribution

The paper presents CBDC, integrating statistical learning, topological data analysis, and Bayesian conformal inference to provide theoretical guarantees and calibrated uncertainty in dermatological classification.

Findings

Achieves high classification accuracy on dermatology datasets.

Provides well-calibrated, interpretable uncertainty estimates.

Guarantees topological stability of CNN embeddings.

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.