Adaptive Deep Fusion Network For Multi-Modal Cancer Progression Prediction: A Scalable and Real-Time Approach

Abstract It is important to have an accurate prognosis of the course of cancer to facilitate early intervention and personalized treatment. It leads to improved patient outcomes. However, existing methods frequently encountered with scalability, class imbalance, multi method data integration and computation efficiency which limits their applicability in real time medical situations. Generally, these existing methods are not effective. The objective is to present a novel framework known as the Adaptive Deep Fusion Network (ADFN) to overcome the existing methods. It utilizes the adaptive learning strategies and deep fusion method for predicting the progression of cancer. A hierarchical multi method fusion mechanism is employed into the ADFN method. This mechanism combines the data from different sources such as clinical records, histopathological data and genomic data. The utilization of attention-based layers to prioritize key features allows this integration and enhancing the prediction accuracy when balancing the computing complexity. To overcome the issues of class imbalance, the ADFN makes use of cost sensitive loss function and targeted loss layers. This method ensures accurate prediction of cancer stages which are essential. Furthermore, an adaptive learning method is based on learning continuously updates method parameters which enable the ADFN to handle large scale dataset efficiently without the human intervention. The method is able to provide the real time prediction because it reduces the amount of computational load by replacing lightweight transformer for typical recurrent neural network (RNN). The ADFN method exceeds existing methods in terms of accuracy, precision, recall and f1-score in experimental evaluations using cancer datasets. As a result, it is reliable for the real time clinical settings and monitors the evolution of cancer, provide proactive treatment methods and efficient method in cancer management.