Alaca, YAkmeşe, ÖF2026-03-312026-03-3120250899-94571098-1098http://dx.doi.org/10.1002/ima.70040https://hdl.handle.net/11491/9485Pancreatic cancer is one of the most aggressive types of cancer, known for its high mortality rate, as it is often diagnosed at an advanced stage. Early diagnosis holds the potential to prolong patients' lifespans and improve treatment success rates. In this study, an innovative method is proposed to enhance the diagnosis of pancreatic cancer. Computed tomography (CT) images were converted into graphs using the Harris Corner Detection Algorithm and analyzed using deep learning models via transfer learning. DenseNet121 and InceptionV3 transfer learning models were trained on graph-based data, and model parameters were optimized using the Whale Optimization Algorithm (WOA). Additionally, classification algorithms such as k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), and Random Forests (RF) were integrated into the analysis of the extracted features. The best results were achieved using the k-NN classification algorithm on features optimized by WOA, yielding an accuracy of 92.10% and an F1 score of 92.74%. The study demonstrated that graph-based transformation enabled more effective modeling of spatial relationships, thereby enhancing the performance of deep learning models. WOA offered significant superiority compared to other methods in parameter optimization. This study aims to contribute to the development of a reliable diagnostic system that can be integrated into clinical applications. In the future, the use of larger and more diverse datasets, along with different graph-based methods, could enhance the generalizability and performance of the proposed approach. The proposed model has the potential to serve as a decision support tool for physicians, particularly in early diagnosis, offering an opportunity to improve patients' quality of life.enconvolutional neural networks (CNN)CT imagesgraph representationHarris Detection Algorithmpancreatic tumorWhale Optimization AlgorithmArticle35210.1002/ima.70040WOS:001467510900001