University of GuilanComputational Sciences and Engineering2783-25035220260102Improving Breast Cancer Diagnosis in Ultrasound Images Using a Multi-Stage Approach with Modified U-Net233244933310.22124/cse.2026.31974.1132ENAliGhanbari SorkhiDepartment of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Mazandaran, IranJournal Article20251014Given the importance of breast cancer detection and the increasing prevalence of this disease, along with its high annual mortality rate, extensive research has been conducted in recent years on medical image analysis for this purpose. In this paper, a multi-stage method is presented based on image segmentation of healthy and unhealthy (cancerous) tissues and a hybrid classification approach for determining the type of cancer (benign or malignant). In the proposed method, after noise reduction, an improved U-Net model is employed for image segmentation and detection of tumor candidate regions. For images identified as unhealthy, contour-based feature extraction is applied, followed by a hybrid ensemble classification method using majority voting among base classifiers to determine the cancer type.
The proposed approach has been evaluated on a standard ultrasound image dataset consisting of healthy, benign, and malignant samples. The proposed method achieved a segmentation accuracy of 97.43% using the enhanced U-Net and an overall system accuracy of 94% for breast cancer diagnosis, outperforming other recent state-of-the-art techniques on the same dataset.https://cse.guilan.ac.ir/article_9333_2f050b718f56e537be0021be74706597.pdfUniversity of GuilanComputational Sciences and Engineering2783-25035220260210Simulation of fluid flow in a counter-flow heat exchanger with partly elastic intermediate walls245265943010.22124/cse.2026.32640.1149ENSeyed EsmailRazaviDepartment of Mechanical Engineering, University of Tabriz, Tabriz, IranTohidAdibiDepartment of Mechanical Engineering, University of Bonab, Bonab, IranJournal Article20251225Heat exchangers are a major component of many heat-related systems, and improving their heat transfer efficiency is a significant engineering challenge. Though the problems of geometric optimization and vibration-assisted mechanisms have been widely discussed, little has been done to examine the impact of partly elastic intermediate walls and oscillatory behavior in counter-flow configurations. This paper numerically simulates the fluid flow and heat transfer in a counter-flow plate heat exchanger with partially elastic intermediate walls with a fully coupled fluid-structure interaction (FSI) methodology. The thermal and hydrodynamic performance of an elastic wall location, oscillation mode, frequency, and amplitude are analyzed. They indicate that the proposed configurations are highly effective in promoting heat transfer, where the increase in Nusselt number is between 23% and 83% in the optimal mid-uniform oscillation scenario. When the oscillation frequency changes to 3 Hz, the Nusselt number is increased by as much as 48 percent, indicating that oscillating elastic walls have a great potential in enhancing heat transfer. Considering the importance of the overall thermo-hydraulic performance (PEC), the performance evaluation criterion was also calculated, confirming that the optimal configurations provide a net performance benefit, with PEC values exceeding 1.0 and reaching up to 1.29.https://cse.guilan.ac.ir/article_9430_1e611876a151f9d80bdf2990bcf1e97f.pdfUniversity of GuilanComputational Sciences and Engineering2783-25035220260213CoGY-Net: An Efficient AI-Powered Shelf OOS Detection System267286944410.22124/cse.2026.32660.1150ENMohsenEshghanmalekDepartment of Computer Engineering, Faculty of Engineering, Yazd University, Yazd, IranValiDerhamiDepartment of Computer Engineering, Faculty of Engineering, Yazd University, Yazd, IranMohammadGhasemzadehDepartment of Computer Engineering, Faculty of Engineering, Yazd University, Yazd, Iran0000-0002-6805-4852Journal Article20251227In modern retail management, there is a high demand for being able to efficiently identify empty shelves. To address the inherent limitations of current monitoring systems in handling dense arrangements and geometrically diverse products, To address this image processing problem, we propose CoGY-Net—a robust, intelligent Out-of-Stock (OOS) detection framework that takes RGB images of retail shelves as input. Our approach significantly enhances the standard YOLO architecture through two primary innovations. First, we integrate the Contourlet Transform as a geometric pre-processor to improve the extraction of curved product features within cluttered backgrounds, leveraging its superior directionality over traditional transforms. Second, we employ the Golden Eagle Optimizer (GEO), a metaheuristic algorithm, to eliminate the inefficiency of manual tuning by autonomously identifying the ideal training hyperparameters and anchor boxes tailored to the specific dataset. Furthermore, to ensure the system remains reliable across varying shelf depths, we implemented a scale-invariant dynamic gap analysis logic to pinpoint empty spaces accurately. In this way we manage to fill the research gap which was the lack of an automated and geometry-aware detection framework capable of handling dense shelf layout and curved products in real environments. The system was evaluated on the Out-Of-Stock-23 dataset. Experimental results demonstrate that CoGY-Net achieves an accuracy 90% and provides a high-precision, automated solution with superior stability, making it highly suitable for seamless integration into real-time smart retail environments and autonomous inventory systems. https://cse.guilan.ac.ir/article_9444_00e7e9b04b491c5cca33c888fc0a4c92.pdf