{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:50:41Z","timestamp":1787673041658,"version":"build-2736575974"},"reference-count":54,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"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: Hepatocyte ballooning (HB) is a significant histological characteristic linked to the advancement of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Although clinicians now consider liver biopsy the most reliable method for identifying HB, its invasive nature and related dangers highlight the need for the development of non-invasive diagnostic options. Objective: This study aims to develop a novel methodology that combines deep learning and machine learning techniques to accurately identify and measure hepatobiliary abnormalities in liver ultrasound images. Methods: The research team expanded the dataset, consisting of ultrasound images, and used it for training deep convolutional neural networks (CNNs) such as InceptionV3, ResNet50, DenseNet121, and EfficientNetB0. A hybrid approach, combining InceptionV3 for feature extraction with a Random Forest classifier, emerged as the most accurate and stable method. An approach of dual dichotomy classification was used to categorize images into two stages: healthy vs. sick, and then mild versus severe ballooning.. Features obtained from CNNs were integrated with conventional machine learning classifiers like Random Forest and Support Vector Machines (SVM). Results: The hybrid approach achieved an accuracy of 97.40%, an area under the curve (AUC) of 0.99, and a sensitivity of 99% for the \u2018Many\u2019 class during the third phase of evaluation. The dual dichotomy classification enhanced the sensitivity in identifying severe instances of HB. The cross-validation process confirmed the strength and reliability of the suggested models. Conclusions: These results indicate that this combination method can decrease the need for invasive liver biopsies by providing a non-invasive and precise alternative for early identification and monitoring of NAFLD and NASH. Subsequent research will prioritize the validation of these models using larger datasets from multiple centers to evaluate their generalizability and incorporation into clinical practice.<\/jats:p>","DOI":"10.3390\/diagnostics14232646","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T05:21:32Z","timestamp":1732512092000},"page":"2646","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Hybrid Deep Learning and Machine Learning for Detecting Hepatocyte Ballooning in Liver Ultrasound Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7797-0417","authenticated-orcid":false,"given":"Fahad","family":"Alshagathrh","sequence":"first","affiliation":[{"name":"College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6082-7873","authenticated-orcid":false,"given":"Mahmood","family":"Alzubaidi","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Gec\u00edk","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Informatics, Technical University of Ko\u0161ice, Letn\u00e1 9, 042 00 Ko\u0161ice, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalid","family":"Alswat","sequence":"additional","affiliation":[{"name":"Liver Disease Research Center, College of Medicine, King Saud University, Riyadh 11461, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Aldhebaib","sequence":"additional","affiliation":[{"name":"Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1917-4871","authenticated-orcid":false,"given":"Bushra","family":"Alahmadi","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, King Abdulaziz Medical City, Riyadh 11426, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0627-8551","authenticated-orcid":false,"given":"Meteb","family":"Alkubeyyer","sequence":"additional","affiliation":[{"name":"Radiology Department, King Saud University Medical City, Riyadh 11472, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulaziz","family":"Alosaimi","sequence":"additional","affiliation":[{"name":"Medical Imaging Department, King Abdulaziz Medical City, Riyadh 11246, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amani","family":"Alsadoon","sequence":"additional","affiliation":[{"name":"Liver Disease Research Center, College of Medicine, King Saud University, Riyadh 11461, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maram","family":"Alkhamash","sequence":"additional","affiliation":[{"name":"Liver Disease Research Center, College of Medicine, King Saud University, Riyadh 11461, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Schneider","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mowafa","family":"Househ","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. 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