Ensemble-Based Deep Learning Framework for Radiotherapy Dose Prediction

Precise dose estimation in radiotherapy is important for the optimization of treatment planning and patient safety. This article introduces an ensemble-based deep learning model that combines several Convolutional Neural Network (CNN) architectures, such as U-Net, DenseNet, ResUNet, and Generative Adversarial Networks (GANs), to improve dose distribution accuracy. Each model is separately trained on augmented medical imaging data, extracting unique spatial and contextual features required for accurate dose estimation. The ensemble blends the outputs of individual models based on a weighted average method with greater impact on better-performing models. The hybrid process capitalizes on the strengths of segmentation-based and generative models to create a robust data-driven dose prediction system. Experimental evidence shows that the presented ensemble performs better than single-model methodologies in dose conformity, homogeneity, and organ-at-risk (OAR) sparing and is thus an effective solution for enhancing automated radio- therapy treatment planning. The modularity and scalability of the framework also facilitate integration into clinical workflows, assisting radiation oncologists in the provision of customized, high-precision treatment