SSDA_AOA: Stacked Sparse Denoising Autoencoder With Archimedes Optimization Algorithm Based Oral Cancer Detection on Histopathological Images.

OBJECTIVES

This study aims to address the challenges in the diagnosis of oral cancer by proposing a novel computer-aided diagnostic framework that leverages advanced deep learning (DL) and optimization techniques to enhance early detection and improve patient outcomes.

MATERIALS AND

METHODS

In the framework proposed, the histopathological images are subjected to a preprocessing technique, and then, the images are fed directly to the NASNet-Large model for the extraction of high-level discriminative texture features. The resultant vectors obtained from the features extracted act as input to the search space of Archimedes Optimization Algorithm that carries out dimensionality reduction and optimal hyperparameter tuning simultaneously. The optimized feature subset is fed to the final classifier, namely the Stacked Sparse Denoising Autoencoder that learns robust latent representations.

RESULTS

The findings demonstrate that the proposed approach achieves superior performance, achieving an accuracy of 95.38%, a precision of 95.15%, a sensitivity of 91.78%, a specificity of 91.85%, and an F1-score of 93.72%.

CONCLUSIONS

These findings underscore the potential of the SSDA-AOA framework as an effective tool for the early detection and precise classification of oral cancer, paving the way for improved patient outcomes through timely intervention.

CLINICAL RELEVANCE: This innovative approach may significantly enhance patient outcomes by facilitating earlier diagnosis and treatment, addressing the urgent need for more reliable diagnostic tools in oncology.