A Novel Pancreatic Tumor Detection and Diagnosis Using Adaptive TransResUnet Aided Segmentation and ASPP with Multi-Scale EfficientNet-Based Classification

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dc.contributor.authorAthab, Naama Methab
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.contributor.authorNaseri, Raghda Awad Shaban
dc.contributor.authorFarhan, Hameed Mutlag
dc.date.accessioned2025-08-14T17:45:37Z
dc.date.available2025-08-14T17:45:37Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.descriptionCODEN : CYSYD
dc.description.abstractA deadly disease with poor prognosis procedure available at present is the pancreatic tumor. Efficient detection is done using a Computer-Aided Diagnosis (CAD) system. The early detection of pancreatic tumors can enhance the survival rate. However, no sufficient works are dedicated to detect pancreatic tumors at its beginning stages. Hence, an advanced deep learning-oriented segmentation process to assist in the detection of pancreatic tumor is developed in this work. The necessary CT and MRI images are gathered from the utilization of IoT-based devices. Once the input image is gathered, the segmentation is carried out. An Adaptive TransResUnet (ATResUNet) is utilized for the segmentation procedure. The variables in the ATResUNet are tuned with the help of Improved African Vultures Optimization Algorithm (IAVOA). The segmented image is further considered to crop the Region of Interest (ROI). The cropped ROI is finally given as input to the suggested Atrous Spatial Pyramid Pooling-based Multi-scale EfficientNet with Attention Mechanism (ASPP-MENetAM) model. The detection of the pancreatic tumor is carried out using the ASPP-MENetAM framework. The detection outcome from the implemented ASPP-MENetAM is then compared with the results from other conventional pancreatic tumor detection models to assess the efficacy of the implemented detection system.
dc.identifier.citationAthab, N. M., Ibrahim, A. A., Naseri, R. A. S., & Farhan, H. M. (2025). A Novel Pancreatic Tumor Detection and Diagnosis Using Adaptive TransResUnet Aided Segmentation and ASPP with Multi-Scale EfficientNet-Based Classification. Cybernetics and Systems, 1-48. 10.1080/01969722.2025.2488820
dc.identifier.doi10.1080/01969722.2025.2488820
dc.identifier.issn0196-9722
dc.identifier.scopus2-s2.0-105007013732
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5915
dc.identifier.wosWOS:001497319900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorAthab, Naama Methab
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.institutionauthorNaseri, Raghda Awad Shaban
dc.institutionauthorFarhan, Hameed Mutlag
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.relation.ispartofCybernetics and Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectadaptive TransResUnet
dc.subjectatrous spatial pyramid pooling-based multi-scale EfficientNet with attention mechanism improved African vultures optimization algorithm
dc.subjectPancreatic tumor detection
dc.subjectROI cropping
dc.titleA Novel Pancreatic Tumor Detection and Diagnosis Using Adaptive TransResUnet Aided Segmentation and ASPP with Multi-Scale EfficientNet-Based Classification
dc.typeArticle

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