{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T15:17:01Z","timestamp":1786547821897,"version":"build-2736575974"},"reference-count":54,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"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>Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%.<\/jats:p>","DOI":"10.3390\/diagnostics13101744","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T02:20:13Z","timestamp":1684203613000},"page":"1744","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method"],"prefix":"10.3390","volume":"13","author":[{"given":"Ahmed Mahdi","family":"Obaid","sequence":"first","affiliation":[{"name":"CEMLab, National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax 3029, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4314-3541","authenticated-orcid":false,"given":"Amina","family":"Turki","sequence":"additional","affiliation":[{"name":"CEMLab, National Engineering School of Sfax, University of Sfax, Sfax 3029, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hatem","family":"Bellaaj","sequence":"additional","affiliation":[{"name":"ReDCAD, National Engineering School of Sfax, University of Sfax, Sfax 3029, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9928-8643","authenticated-orcid":false,"given":"Mohamed","family":"Ksantini","sequence":"additional","affiliation":[{"name":"CEMLab, National Engineering School of Sfax, University of Sfax, Sfax 3029, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulla","family":"AlTaee","sequence":"additional","affiliation":[{"name":"Croydon Hospital, London CR7 7YE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2926-6083","authenticated-orcid":false,"given":"Alaa","family":"Alaerjan","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"ref_1","unstructured":"(2023, April 13). Gallbladder Disease. Available online: https:\/\/englewoodgi.com\/conditions-and-diseases\/gallbladder-disease\/."},{"key":"ref_2","unstructured":"American Cancer Society (2021, October 04). Gallbladder Cancer Risk Factors. 29 March 2021. Available online: https:\/\/www.cancer.org\/cancer\/gallbladder-cancer\/causes-risks-prevention\/risk-factors.html."},{"key":"ref_3","unstructured":"American Society of Clinical Oncology (2021, October 04). Gallbladder Cancer: Risk Factors and Prevention. Available online: https:\/\/www.cancer.net\/cancer-types\/gallbladder-cancer\/risk-factors-and-prevention."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s10396-021-01093-3","article-title":"Everything you need to know about ultrasound for diagnosis of gallbladder diseases","volume":"48","author":"Okaniwa","year":"2021","journal-title":"J. Med. Ultrason."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"155","DOI":"10.2478\/s11772-011-0001-y","article-title":"Computer analysis of gallbladder ultrasonic images towards recognition of pathological lesions","volume":"19","author":"Ogiela","year":"2011","journal-title":"Opto-Electron. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11045-021-00765-0","article-title":"Fetal biometric based abnormality detection during prenatal development using deep learning techniques","volume":"33","author":"Selvathi","year":"2022","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"8786793","DOI":"10.34133\/2021\/8786793","article-title":"Advances in Deep Learning-Based Medical Image Analysis","volume":"2021","author":"Liu","year":"2021","journal-title":"Health Data Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s11036-020-01672-7","article-title":"A review of deep learning on medical image analysis","volume":"26","author":"Wang","year":"2021","journal-title":"Mob. Netw. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","article-title":"A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises","volume":"109","author":"Zhou","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_14","unstructured":"Shinde, S., Kulkarni, U., Mane, D., and Sapkal, A. (2021). Health Informatics: A Computational Perspective in Healthcare, Springer. Studies in Computational Intelligence."},{"key":"ref_15","unstructured":"Hatture, S.M., and Kadakol, N. (2021). Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics, Academic Press."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1053\/j.gastro.2008.12.031","article-title":"Roles of Infection, Inflammation, and the Immune System in Cholesterol Gallstone Formation","volume":"136","author":"Maurer","year":"2009","journal-title":"Gastroenterology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"172","DOI":"10.5009\/gnl.2012.6.2.172","article-title":"Epidemiology of Gallbladder Disease: Cholelithiasis and Cancer","volume":"6","author":"Stinton","year":"2012","journal-title":"Gut Liver"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"16024","DOI":"10.1038\/nrdp.2016.24","article-title":"Gallstones","volume":"2","author":"Lammert","year":"2016","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Doherty, G., Manktelow, M., Skelly, B., Gillespie, P., Bjourson, A.J., and Watterson, S. (2022). The Need for Standardizing Diagnosis, Treatment and Clinical Care of Cholecystitis and Biliary Colic in Gallbladder Disease. Medicina, 58.","DOI":"10.3390\/medicina58030388"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1001\/jama.2022.2350","article-title":"Acute cholecystitis: A review","volume":"327","author":"Gallaher","year":"2022","journal-title":"JAMA"},{"key":"ref_21","unstructured":"Jones, M.W., Genova, R., and O\u2019Rourke, M.C. (2022, October 24). Acute cholecystitis, StatPearls, Available online: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK459171\/."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7832","DOI":"10.3748\/wjg.v12.i48.7832","article-title":"Diagnosis and treatment of gallbladder perforation","volume":"12","author":"Derici","year":"2006","journal-title":"World J. Gastroenterol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1177\/8756479316631535","article-title":"Emphysematous cholecystitis: A deadly twist to a common disease","volume":"32","author":"Mishal","year":"2016","journal-title":"J. Diagn. Med. Sonogr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/978-3-319-57403-5_32","article-title":"Cholecystitis","volume":"9","author":"Zafar","year":"2018","journal-title":"Geriatr. Trauma Acute Care Surg."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"40","DOI":"10.5005\/jp-journals-10018-1294","article-title":"Diagnosis and Treatment of Gallbladder Polyps: Current Perspectives","volume":"9","author":"Dilek","year":"2019","journal-title":"Euroasian J. Hepato-Gastroenterol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.jviscsurg.2017.06.004","article-title":"Gallbladder adenomyomatosis: Diagnosis and management","volume":"154","author":"Golse","year":"2017","journal-title":"J. Visc. Surg."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1111\/j.1440-1746.2011.07048.x","article-title":"Gallbladder cancer: Can newer insights improve the outcome?","volume":"27","author":"Dutta","year":"2012","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1080\/17474124.2017.1292126","article-title":"Diagnostic efficacy of ultrasound in hepatocellular carcinoma diagnosis","volume":"11","author":"Kee","year":"2017","journal-title":"Expert Rev. Gastroenterol. Hepatol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s10396-020-01012-y","article-title":"Current role of ultrasound in the diagnosis of hepatocellular carcinoma","volume":"47","author":"Tanaka","year":"2020","journal-title":"J. Med. Ultrason."},{"key":"ref_30","first-page":"CD012233","article-title":"Transabdominal ultrasound or endoscopic ultrasound for diagnosis of gallbladder polyps","volume":"8","author":"Wennmacker","year":"2016","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhu, H., and Yang, T. (2019, January 3\u20135). Deep neural networks for fatty liver ultrasound images classification. Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019, Nanchang, China.","DOI":"10.1109\/CCDC.2019.8833364"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"75.e1","DOI":"10.1016\/j.jpurol.2018.10.020","article-title":"Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features","volume":"15","author":"Zheng","year":"2019","journal-title":"J. Pediatr. Urol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Arora, H., and Mittal, N. (2019, January 12\u201314). Image Enhancement Techniques for Gastric Diseases Detection using Ultrasound Images. Proceedings of the 3rd International Conference on Electronics and Communication and Aerospace Technology, ICECA 2019, Coimbatore, India.","DOI":"10.1109\/ICECA.2019.8822148"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Precious, J.G., and Selvan, S. (2018, January 1\u20133). Detection of Abnormalities in Ultrasound Images Using Texture and Shape Features. Proceedings of the 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), Coimbatore, India.","DOI":"10.1109\/ICCTCT.2018.8551174"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1049\/iet-ipr.2016.1072","article-title":"Hybrid approach to classification of focal and diffused liver disorders using ultrasound images with wavelets and texture features","volume":"11","author":"Krishnan","year":"2017","journal-title":"IET Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"22829","DOI":"10.1109\/ACCESS.2019.2898121","article-title":"A Novel Algorithm for Breast Lesion Detection Using Textons and Local Configuration Pattern Features with Ultrasound Imagery","volume":"7","author":"Acharya","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","article-title":"Deep Learning in Medical Ultrasound Analysis: A Review","volume":"5","author":"Liu","year":"2019","journal-title":"Engineering"},{"key":"ref_38","first-page":"81","article-title":"Medical Image Analysis Using Deep Learning: A Systematic Literature Review","volume":"985","author":"Kumar","year":"2019","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_39","first-page":"11","article-title":"Deep Learning in Ultrasound Imaging","volume":"108","author":"Cohen","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4103\/jofs.jofs_55_19","article-title":"The Use of Deep Convolutional Neural Networks in Biomedical Imaging: A Review","volume":"11","author":"Mupparapu","year":"2019","journal-title":"J. Orofac. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Urman, J.M., Herranz, J.M., Uriarte, I., Rull\u00e1n, M., Oy\u00f3n, D., Gonz\u00e1lez, B., Fernandez-Uri\u00e9n, I., Carrascosa, J., Bolado, F., and Zabalza, L. (2020). Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach. Cancers, 12.","DOI":"10.3390\/cancers12061644"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.future.2018.11.011","article-title":"A deep learning model for predicting chemical composition of gallstones with big data in medical Internet of Things","volume":"94","author":"Yao","year":"2018","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3955","DOI":"10.1007\/s11227-021-03843-z","article-title":"Adoption of combined detection technology of tumor markers via deep learning algorithm in diagnosis and prognosis of gallbladder carcinoma","volume":"78","author":"Chang","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1038\/s41467-021-21466-z","article-title":"Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images","volume":"12","author":"Zhou","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_45","first-page":"705","article-title":"Detection of Biliary Artesia using Sonographic Gallbladder Images with the help of Deep Learning approaches","volume":"Volume 1","author":"Obaid","year":"2022","journal-title":"Proceedings of the 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)"},{"key":"ref_46","unstructured":"Horry, M., Chakraborty, S., Pradhan, B., Paul, M., Zhu, J., Loh, H.W., Barua, P.D., and Arharya, U.R. (2022). Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"750","DOI":"10.2214\/AJR.16.16128","article-title":"Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield","volume":"208","author":"Hassanpour","year":"2017","journal-title":"Am. J. Roentgenol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2442","DOI":"10.3389\/fneur.2021.742126","article-title":"Multidimensional Deep Learning Reduces False-Positives in the Automated Detection of Cerebral Aneurysms on Time-Of-Flight Magnetic Resonance Angiography: A Multi-Center Study","volume":"12","author":"Terasaki","year":"2022","journal-title":"Front. Neurol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, D., Xiao, C., Liu, Y., Chen, Z., Hassan, H., Su, L., Liu, J., Li, H., Xie, W., and Zhong, W. (2022). Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images. Diagnostics, 12.","DOI":"10.3390\/diagnostics12081788"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1053\/j.semnuclmed.2022.02.003","article-title":"Application of Deep Learning in Breast Cancer Imaging","volume":"52","author":"Balkenende","year":"2022","journal-title":"Semin. Nucl. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/17562848221093873","article-title":"Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: A systemic review","volume":"15","author":"Goyal","year":"2022","journal-title":"Ther. Adv. Gastroenterol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e13695","DOI":"10.1002\/acm2.13695","article-title":"Deep learning for emergency ascites diagnosis using ultrasonography images","volume":"23","author":"Lin","year":"2022","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102331","DOI":"10.1016\/j.artmed.2022.102331","article-title":"Deep learning for image-based liver analysis\u2014A comprehensive review focusing on malignant lesions","volume":"130","author":"Survarachakan","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"ref_54","unstructured":"Olaf, R., Fischer, P., and Brox, T. (2015). Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany, 5\u20139 October 2015, Springer International Publishing. Part III 18."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/10\/1744\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:35:26Z","timestamp":1760124926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/10\/1744"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,15]]},"references-count":54,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["diagnostics13101744"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13101744","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,15]]}}}