{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:56:56Z","timestamp":1792807016996,"version":"4.5.0"},"reference-count":31,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Early detection and diagnosis are important when treating gallbladder (GB) diseases. Poorer clinical outcomes and increased patient symptoms may result from any error or delay in diagnosis. Many signs and symptoms, especially those related to GB diseases with similar symptoms, may be unclear. Therefore, highly qualified medical professionals should interpret and understand ultrasound images. Considering that diagnosis via ultrasound imaging can be time- and labor-consuming, it may be challenging to finance and benefit from this service in remote locations. Methods: Today, artificial intelligence (AI) techniques ranging from machine learning (ML) to deep learning (DL), especially in large datasets, can help analysts using Content-Based Image Retrieval (CBIR) systems with the early diagnosis, treatment, and recognition of diseases, and then provide effective methods for a medical diagnosis. Results: The developed model is compared with two different textural and six different Convolutional Neural Network (CNN) models accepted in the literature\u2014the developed model combines features obtained from three different pre-trained architectures for feature extraction. The cosine method was preferred as the similarity measurement metric. Conclusions: Our proposed CBIR model achieved successful results from six other different models. The AP value obtained in the proposed model is 0.94. This value shows that our CBIR-based model can be used to detect GB diseases.<\/jats:p>","DOI":"10.3390\/diagnostics15050552","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T03:40:12Z","timestamp":1740454812000},"page":"552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1973-2511","authenticated-orcid":false,"given":"Ahmet","family":"Bozdag","sequence":"first","affiliation":[{"name":"Department of General Surgery, School of Medicine, Firat University, Elaz\u0131\u011f 23119, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1866-4721","authenticated-orcid":false,"given":"Muhammed","family":"Yildirim","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Malatya Turgut Ozal University, Malatya 44210, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8087-4044","authenticated-orcid":false,"given":"Mucahit","family":"Karaduman","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Malatya Turgut Ozal University, Malatya 44210, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hursit Burak","family":"Mutlu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Malatya Turgut Ozal University, Malatya 44210, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8034-3019","authenticated-orcid":false,"given":"Gulsah","family":"Karaduman","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Firat University, Elaz\u0131\u011f 23119, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9683-6691","authenticated-orcid":false,"given":"Aziz","family":"Aksoy","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, Malatya Turgut Ozal University, Malatya 44200, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1148\/rg.2021200087","article-title":"Gallbladder carcinoma and its differential diagnosis at MRI: What radiologists should know","volume":"41","author":"Vendrami","year":"2021","journal-title":"Radiographics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1080\/00365521.2016.1182583","article-title":"Determinants for gallstone formation--a new data cohort study and a systematic review with meta-analysis","volume":"51","author":"Shabanzadeh","year":"2016","journal-title":"Scand. J. Gastroenterol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Obaid, A.M., Turki, A., Bellaaj, H., Ksantini, M., AlTaee, A., and Alaerjan, A. (2023). Detection of gallbladder disease types using deep learning: An informative medical method. Diagnostics, 13.","DOI":"10.3390\/diagnostics13101744"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1055\/s-2006-960174","article-title":"Genetic predisposition to gallbladder stones","volume":"44","author":"Wittenburg","year":"2007","journal-title":"Semin. Liver Dis."},{"key":"ref_5","first-page":"e66453","article-title":"Advancements in Cholelithiasis Diagnosis: A Systematic Review of Machine Learning Applications in Imaging Analysis","volume":"16","author":"Ahmed","year":"2024","journal-title":"Cureus"},{"key":"ref_6","unstructured":"Dvora, B. (2025, January 15). Gallbladder Cancer. Available online: https:\/\/tamc.co.il\/en\/article\/gallbladder-cancer."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S124","DOI":"10.1016\/j.jhep.2008.01.012","article-title":"Gallstone disease: From genes to evidence-based therapy","volume":"48","author":"Lammert","year":"2008","journal-title":"J. Hepatol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s10654-017-0263-x","article-title":"Screen-detected gallstone disease and cardiovascular disease","volume":"32","author":"Shabanzadeh","year":"2017","journal-title":"Eur. J. Epidemiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1111\/jgh.14264","article-title":"Gallstone disease and increased risk of mortality: Two large prospective studies in US men and women","volume":"33","author":"Zheng","year":"2018","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1016\/j.cgh.2024.01.051","article-title":"Global Epidemiology of Gallstones in the 21st Century: A systematic review and Meta-analysis","volume":"22","author":"Wang","year":"2024","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"99994","DOI":"10.4251\/wjgo.v17.i1.99994","article-title":"Gallbladder carcinoma in the era of artificial intelligence: Early diagnosis for better treatment","volume":"17","author":"Burud","year":"2025","journal-title":"World J. Gastrointest. Oncol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.asjsur.2018.03.005","article-title":"Reasonable cholecystectomy of gallbladder polyp--10 years of experience","volume":"42","author":"Lee","year":"2019","journal-title":"Asian J. Surg."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1631\/FITEE.1601885","article-title":"Applications of artificial intelligence in intelligent manufacturing: A review","volume":"18","author":"Li","year":"2017","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s44163-023-00049-5","article-title":"Evaluation of artificial intelligence techniques in disease diagnosis and prediction","volume":"3","author":"Nia","year":"2023","journal-title":"Discov. Artif. Intell."},{"key":"ref_15","unstructured":"Barata, C., and Santiago, C. (October, January 27). Improving the explainability of skin cancer diagnosis using CBIR. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102239","DOI":"10.1016\/j.compmedimag.2023.102239","article-title":"A clinically motivated self-supervised approach for content-based image retrieval of CT liver images","volume":"107","author":"Radiya","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lo, C.-M., Wang, C.-C., and Hung, P.-H. (2024). Interactive content-based image retrieval with deep learning for CT abdominal organ recognition. Phys. Med. Biol., 69.","DOI":"10.1088\/1361-6560\/ad1f86"},{"key":"ref_18","first-page":"100279","article-title":"Deep-learning enabled ultrasound based detection of gallbladder cancer in northern India: A prospective diagnostic study","volume":"24","author":"Gupta","year":"2024","journal-title":"Lancet Reg. Heal. Asia"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhou, W., Ye, Z., Huang, G., Zhang, X., Xu, M., Liu, B., Zhuang, B., Tang, Z., Wang, S., and Chen, D. (2024). Interpretable artificial intelligence-based app assists inexperienced radiologists in diagnosing biliary atresia from sonographic gallbladder images. BMC Med., 22.","DOI":"10.1186\/s12916-024-03247-9"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yu, C.-J., Yeh, H.J., Chang, C.C., Tang, J.H., Kao, W.Y., Chen, W.C., Huang, Y.J., Li, C.H., Chang, W.H., and Lin, Y.T. (2021). Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound. Comput. Methods Programs Biomed., 211.","DOI":"10.1016\/j.cmpb.2021.106382"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dadjouy, S., and Sajedi, H. (2024, January 15\u201317). Gallbladder Cancer Detection in Ultrasound Images based on YOLO and Faster R-CNN. Proceedings of the 2024 10th International Conference on Artificial Intelligence and Robotics (QICAR), Singapore.","DOI":"10.1109\/QICAR61538.2024.10496645"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e37258","DOI":"10.1097\/MD.0000000000037258","article-title":"Early prediction of gallstone disease with a machine learning-based method from bioimpedance and laboratory data","volume":"103","author":"Esen","year":"2024","journal-title":"Medicine"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2171","DOI":"10.3748\/wjg.v11.i14.2171","article-title":"Outcome of gall bladder polypoidal lesions detected by transabdominal ultrasound scanning: A nine year experience","volume":"11","author":"Chattopadhyay","year":"2005","journal-title":"World J. Gastroenterol. WJG"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pang, S., Wang, S., Rodr\u00edguez-Pat\u00f3n, A., Li, P., and Wang, X. (2019). An artificial intelligent diagnostic system on mobile Android terminals for cholelithiasis by lightweight convolutional neural network. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0221720"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3548","DOI":"10.1111\/jgh.15673","article-title":"Diagnostic performance of endoscopic ultrasound-artificial intelligence using deep learning analysis of gallbladder polypoid lesions","volume":"36","author":"Jang","year":"2021","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_26","first-page":"1661","article-title":"Context based healthcare informatics system to detect gallstones using deep learning methods","volume":"9","author":"Veena","year":"2022","journal-title":"Int. J. Adv. Technol. Eng. Explor."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"166823","DOI":"10.1109\/ACCESS.2019.2953934","article-title":"U-next: A novel convolution neural network with an aggregation u-net architecture for gallstone segmentation in ct images","volume":"7","author":"Song","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110426","DOI":"10.1016\/j.dib.2024.110426","article-title":"UIdataGB: Multi-Class ultrasound images dataset for gallbladder disease detection","volume":"54","author":"Turki","year":"2024","journal-title":"Data Br."},{"key":"ref_29","unstructured":"Turki, A., Mahdi Obaid, A., Bellaaj, H., Ksantini, M., and Altaee, A. (2024). Gallblader Diseases Dataset. Mendeley Data."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lahitani, A.R., Permanasari, A.E., and Setiawan, N.A. (2016, January 26\u201327). Cosine similarity to determine similarity measure: Study case in online essay assessment. Proceedings of the 2016 4th International conference on cyber and IT service management, Bandung, Indonesia.","DOI":"10.1109\/CITSM.2016.7577578"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"187","DOI":"10.3892\/etm.2021.11110","article-title":"Systematic review of the role of indocyanine green near-infrared fluorescence in safe laparoscopic cholecystectomy","volume":"23","author":"Serban","year":"2022","journal-title":"Exp. Ther. Med."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/5\/552\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:41:51Z","timestamp":1760028111000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/5\/552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,25]]},"references-count":31,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["diagnostics15050552"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15050552","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,25]]}}}