Baltaci, HakanYalcin, SercanYildirim, MuhammedBingol, Harun2026-06-192026-06-1920252075-4418https://doi.org/10.3390/diagnostics15192476https://hdl.handle.net/20.500.12899/5221Background/Objectives: Diagnosis of abdominal aortic aneurysm and abdominal aortic dissection (AAA and AAD) is of strategic importance as cardiovascular disease has fatal implications worldwide. This study presents a novel deep learning-based approach for the accurate and efficient diagnosis of abdominal aortic aneurysms (AAAs) and aortic dissections (AADs) from CT images. Methods: Our proposed convolutional neural network (CNN) architecture effectively extracts relevant features from CT scans and classifies regions as normal or diseased. Additionally, the model accurately delineates the boundaries of detected aneurysms and dissections, aiding in clinical decision-making. A pyramid scene parsing network has been built in a hybrid method. The layer block after the classification layer is divided into two groups: whether there is an AAA or AAD region in the abdominal CT image, and determination of the borders of the detected diseased region in the medical image. Results: In this sense, both detection and segmentation are performed in AAA and AAD diseases. Python programming has been used to assess the accuracy and performance results of the proposed strategy. From the results, average accuracy rates of 83.48%, 86.9%, 88.25%, and 89.64% were achieved using ResDenseUNet, INet, C-Net, and the proposed strategy, respectively. Also, intersection over union (IoU) of 79.24%, 81.63%, 82.48%, and 83.76% have been achieved using ResDenseUNet, INet, C-Net, and the proposed method. Conclusions: The proposed strategy is a promising technique for automatically diagnosing AAA and AAD, thereby reducing the workload of cardiovascular surgeons.eninfo:eu-repo/semantics/openAccessAorticArtificial IntelligenceDeep LearningConvolutional Neural NetworksAneurysm And Dissection DiagnosisArticle10.3390/diagnostics151924761519410956952-s2.0-105019197204Q2WOS:001593854000001Q10000-0001-5512-43080000-0003-1866-4721