Predictive Oncology Deep Learning-Based Multi-Cancer Detection

Abstract

Predictive Oncology using deep learning enables automated multi-cancer detection by extracting complex patterns from high-dimensional clinical and imaging data. Traditional cancer diagnostics are limited by manual feature extraction and variability in interpretation. Deep learning techniques such as convolutional neural networks (CNNs) and transformer models have shown remarkable performance in single-cancer detection tasks. However, existing approaches are generally constrained to a specific cancer type, such as skin cancer, creating a need for scalable multi-cancer frameworks. This research proposes an integrated deep learning model for simultaneous detection of liver, skin, and breast cancers. The model incorporates multi-modal data fusion to enhance generalization across varying cancer presentations. Extensive experiments demonstrate improved accuracy, sensitivity, and robustness compared to traditional methods. The proposed framework supports early detection, aiding clinical decision-making and personalized treatment planning. These results highlight the potential of deep learning-based predictive oncology for broad clinical application and real-world deployment.