Abstract

This study proposes a novel approach for multi-cancer detection utilizing the VGH-6 algorithm coupled with an efficient neural network model and SHAP (Shapley Additive Explanation) AI method. The VGH-6 algorithm is a sophisticated computational tool known for its accuracy in identifying various types of cancer. In this research, it is combined with an efficient neural network architecture to enhance the classification performance and improve the overall detection process. Additionally, the SHAP AI technique is incorporated to provide insightful explanations for the model's predictions, thus increasing the interpretability and trustworthiness of the results. By leveraging these advanced technologies in tandem, the proposed methodology aims to achieve a robust and reliable multi-cancer detection system. The integration of VGH-6, efficient neural networks, and SHAP AI offers a comprehensive framework for accurate cancer identification, which is crucial for early diagnosis and effective treatment planning. Ultimately, this study introduces a promising approach that may significantly impact the field of cancer detection and pave the way for improved healthcare outcomes.