Abstract <p>In computer-aided diagnosis systems, precise feature extraction from CT scans of colorectal cancer using deep learning is essential for effective prognosis. However, existing convolutional neural networks struggle to capture long-range dependencies and contextual information, resulting in incomplete CT feature extraction. To address this, the PEDRA-EFB0 architecture integrates patch embeddings and a dual residual attention mechanism for enhanced feature extraction and survival prediction in colorectal cancer CT scans. A patch embedding method processes CT scans into patches, creating positional features for global representation and guiding spatial attention computation. Additionally, a dual residual attention mechanism during the upsampling stage selectively combines local and global features, enhancing CT data utilization. Furthermore, this paper proposes a feature selection algorithm that combines autoencoders and entropy technology, encoding and compressing high-dimensional data to reduce redundant information and using entropy to assess the importance of features, thereby achieving precise feature selection. Experimental results indicate the PEDRA-EFB0 model outperforms traditional methods on colorectal cancer CT metrics, notably in C-index, BS, MCC, and AUC, enhancing survival prediction accuracy. Our code is freely available at <a href="https://github.com/smile0208z/PEDRA">https://github.com/smile0208z/PEDRA</a>.</p> Graphical abstract <p>A deep learning model is proposed by integrating the PEDRA module into the EfficientNetB0 network to extract global and local features from CT images. These features are then fused and refined via an encoder-decoder structure incorporating an information entropy mechanism. Finally, Cox and DeepSurv are applied to process the features and generate risk scores for survival curve prediction</p>