Detection of Acromion Types in Shoulder Magnetic Resonance Image Examination with Developed Convolutional Neural Network and Textural-Based Content-Based Image Retrieval System

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dc.contributor.authorAkcicek, Mehmet
dc.contributor.authorKaraduman, Muecahit
dc.contributor.authorPetik, Bulent
dc.contributor.authorUnlu, Serkan
dc.contributor.authorMutlu, Hursit Burak
dc.contributor.authorYildirim, Muhammed
dc.date.accessioned2026-06-19T06:37:44Z
dc.date.available2026-06-19T06:37:44Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractBackground: The morphological type of the acromion may play a role in the etiopathogenesis of various pathologies, such as shoulder impingement syndrome and rotator cuff disorders. Therefore, it is important to determine the acromion's morphological types accurately and quickly. In this study, it was aimed to detect the acromion shape, which is one of the etiological causes of chronic shoulder disorders that may cause a decrease in work capacity and quality of life, on shoulder MR images by developing a new model for image retrieval in Content-Based Image Retrieval (CBIR) systems. Methods: Image retrieval was performed in CBIR systems using Convolutional Neural Network (CNN) architectures and textural-based methods as the basis. Feature maps of the images were extracted to measure image similarities in the developed CBIR system. For feature map extraction, feature extraction was performed with Histogram of Gradient (HOG), Local Binary Pattern (LBP), Darknet53, and Densenet201 architectures, and the Minimum Redundancy Maximum Relevance (mRMR) feature selection method was used for feature selection. The feature maps obtained after the dimensionality reduction process were combined. The Euclidean distance and Peak Signal-to-Noise Ratio (PSNR) were used as similarity measurement methods. Image retrieval was performed using features obtained from CNN architectures and textural-based models to compare the performance of the proposed method. Results: The highest Average Precision (AP) value was reached in the PSNR similarity measurement method with 0.76 in the proposed model. Conclusions: The proposed model is promising for accurately and rapidly determining morphological types of the acromion, thus aiding in the diagnosis and understanding of chronic shoulder disorders.
dc.identifier.doi10.3390/jcm14020505
dc.identifier.issn2077-0383
dc.identifier.issue2
dc.identifier.orcid0000-0002-8087-4044
dc.identifier.orcid0009-0009-2176-0192
dc.identifier.orcid0000-0002-0232-1284
dc.identifier.orcid0000-0003-1866-4721
dc.identifier.pmid39860510
dc.identifier.scopus2-s2.0-85216074557
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/jcm14020505
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5196
dc.identifier.volume14
dc.identifier.wosWOS:001404221800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofJournal of Clinical Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectCbir
dc.subjectCnn
dc.subjectFeature Selection
dc.subjectRetrieval
dc.subjectShoulder Acromion
dc.titleDetection of Acromion Types in Shoulder Magnetic Resonance Image Examination with Developed Convolutional Neural Network and Textural-Based Content-Based Image Retrieval System
dc.typeArticle

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