{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T09:00:40Z","timestamp":1778490040974,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Funda\u00e7\u0101o para a Ci\u00eancia e a Tecnologia","award":["UIDB\/00285\/2020"],"award-info":[{"award-number":["UIDB\/00285\/2020"]}]},{"name":"FCT\u2014Funda\u00e7\u0101o para a Ci\u00eancia e a Tecnologia","award":["LA\/P\/0112\/2020"],"award-info":[{"award-number":["LA\/P\/0112\/2020"]}]},{"name":"FCT\u2014Funda\u00e7\u0101o para a Ci\u00eancia e a Tecnologia","award":["UIDB\/00326\/2020"],"award-info":[{"award-number":["UIDB\/00326\/2020"]}]},{"name":"FCT\u2014Funda\u00e7\u0101o para a Ci\u00eancia e a Tecnologia","award":["UIDB\/FIS\/04559\/2020"],"award-info":[{"award-number":["UIDB\/FIS\/04559\/2020"]}]},{"name":"FCT\/MCTES","award":["UIDB\/00285\/2020"],"award-info":[{"award-number":["UIDB\/00285\/2020"]}]},{"name":"FCT\/MCTES","award":["LA\/P\/0112\/2020"],"award-info":[{"award-number":["LA\/P\/0112\/2020"]}]},{"name":"FCT\/MCTES","award":["UIDB\/00326\/2020"],"award-info":[{"award-number":["UIDB\/00326\/2020"]}]},{"name":"FCT\/MCTES","award":["UIDB\/FIS\/04559\/2020"],"award-info":[{"award-number":["UIDB\/FIS\/04559\/2020"]}]},{"name":"European Regional Development Fund (ERDF) through the Portuguese Operational Program for Competitiveness and Internationalization, COMPETE 2020","award":["UIDB\/00285\/2020"],"award-info":[{"award-number":["UIDB\/00285\/2020"]}]},{"name":"European Regional Development Fund (ERDF) through the Portuguese Operational Program for Competitiveness and Internationalization, COMPETE 2020","award":["LA\/P\/0112\/2020"],"award-info":[{"award-number":["LA\/P\/0112\/2020"]}]},{"name":"European Regional Development Fund (ERDF) through the Portuguese Operational Program for Competitiveness and Internationalization, COMPETE 2020","award":["UIDB\/00326\/2020"],"award-info":[{"award-number":["UIDB\/00326\/2020"]}]},{"name":"European Regional Development Fund (ERDF) through the Portuguese Operational Program for Competitiveness and Internationalization, COMPETE 2020","award":["UIDB\/FIS\/04559\/2020"],"award-info":[{"award-number":["UIDB\/FIS\/04559\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious liver diseases. Therefore, accurately diagnosing the condition at an early stage is essential for more effective intervention and management. This study uses images acquired via ultrasound and elastography to classify liver steatosis using classical machine learning classifiers, including random forest and support vector machine, as well as deep learning architectures, such as ResNet50V2 and DenseNet-201. The neural network demonstrated the most optimal performance, achieving an F1 score of 99.5% on the ultrasound dataset, 99.2% on the elastography dataset, and 98.9% on the mixed dataset. The results from the deep learning approach are comparable to those of machine learning, despite objectively not achieving the highest results. This research offers valuable insights into the domain of medical image classification and advocates the integration of advanced machine learning and deep learning technologies in diagnosing steatosis.<\/jats:p>","DOI":"10.3390\/s24237568","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T10:05:56Z","timestamp":1732701956000},"page":"7568","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Ultrasound Versus Elastography in the Diagnosis of Hepatic Steatosis: Evaluation of Traditional Machine Learning Versus Deep Learning"],"prefix":"10.3390","volume":"24","author":[{"given":"Rodrigo","family":"Marques","sequence":"first","affiliation":[{"name":"Faculdade de Ci\u00eancias e Tecnologias, Department of Physics, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4936-9434","authenticated-orcid":false,"given":"Jaime","family":"Santos","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computers Engineering, CEMMPRE-ARISE, University of Coimbra, Polo II, Rua S\u00edlvio Lima, 3030-970 Coimbra, Portugal"}]},{"given":"Alexandra","family":"Andr\u00e9","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Coimbra, Coimbra Health School, 3046-854 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7529-6422","authenticated-orcid":false,"given":"Jos\u00e9","family":"Silva","sequence":"additional","affiliation":[{"name":"Military Academy Research Center (CINAMIL), Portuguese Military Academy, 1169-203 Lisbon, Portugal"},{"name":"LIBPhys, LA-REAL, Faculdade de Ci\u00eancias e Tecnologia, Universidade de Coimbra, 3004-516 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"ref_1","first-page":"167","article-title":"Pathogenesis and Prevention of Hepatic Steatosis","volume":"11","author":"Nassir","year":"2015","journal-title":"Gastroenterol. Hepatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1097\/HEP.0000000000000004","article-title":"The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): A systematic review","volume":"77","author":"Younossi","year":"2023","journal-title":"Hepatology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1159\/000448940","article-title":"Non-Alcoholic Fatty Liver Disease: Cause or Effect of Metabolic Syndrome","volume":"32","author":"Grander","year":"2016","journal-title":"Visc. Med."},{"key":"ref_4","first-page":"213","article-title":"Non-alcoholic Fatty Liver Disease: Diagnosis and Treatment","volume":"23","author":"Anjani","year":"2023","journal-title":"J. Biol. Trop."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7392","DOI":"10.3748\/wjg.v20.i23.7392","article-title":"Radiologic evaluation of nonalcoholic fatty liver disease","volume":"20","author":"Lee","year":"2014","journal-title":"World J. Gastroenterol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1007\/s00261-018-1517-0","article-title":"Machine learning for medical ultrasound: Status, methods, and future opportunities","volume":"43","author":"Brattain","year":"2018","journal-title":"Abdom. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"290","DOI":"10.3350\/cmh.2017.0042","article-title":"Imaging evaluation of non-alcoholic fatty liver disease: Focused on quantification","volume":"23","author":"Lee","year":"2017","journal-title":"Clin. Mol. Hepatol."},{"key":"ref_8","first-page":"539","article-title":"Diagnosis of fatty liver disease: Is biopsy necessary?","volume":"15","author":"Joy","year":"2003","journal-title":"Eur. J. Gastroenterol. Hepatol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"638","DOI":"10.4254\/wjh.v7.i4.638","article-title":"Non-invasive methods for the diagnosis of nonalcoholic fatty liver disease","volume":"7 4","author":"Papagianni","year":"2015","journal-title":"World J. Hepatol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.21037\/qims-21-700","article-title":"Diagnosis of steatohepatitis and fibrosis in biopsy-proven nonalcoholic fatty liver diseases: Including two-dimension real-time shear wave elastography and noninvasive fibrotic biomarker scores","volume":"12","author":"Zhou","year":"2022","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.aohep.2018.09.003","article-title":"Transient hepatic elastography has the best performance to evaluate liver fibrosis in non-alcoholic fatty liver disease (NAFLD)","volume":"18","author":"Tovo","year":"2019","journal-title":"Ann. Hepatol."},{"key":"ref_12","first-page":"169","article-title":"Point Shear Wave Elastography by ElastPQ for Fibrosis Screening in Patients with NAFLD: A Prospective, Multicenter Comparison to Vibration-Controlled Elastography","volume":"44","author":"Bauer","year":"2022","journal-title":"Eur. J. Ultrasound-Ultraschall Der Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e13980","DOI":"10.1111\/eci.13980","article-title":"Performance of two-dimensional shear wave elastography and transient elastography compared to liver biopsy for staging of liver fibrosis","volume":"53","author":"Kovatsch","year":"2023","journal-title":"Eur. J. Clin. Investig."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1097\/JCMA.0000000000000585","article-title":"Deep learning for abdominal ultrasound: A computer-aided diagnostic system for the severity of fatty liver","volume":"84","author":"Chou","year":"2021","journal-title":"J. Chin. Med. Assoc."},{"key":"ref_15","first-page":"135","article-title":"Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images","volume":"23","author":"Constantinescu","year":"2021","journal-title":"Med. Ultrason."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1007\/s11548-018-1843-2","article-title":"Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images","volume":"13","author":"Byra","year":"2018","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.3748\/wjg.v28.i22.2494","article-title":"Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning","volume":"28","author":"Li","year":"2022","journal-title":"World J. Gastroenterol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kagadis, G.C., Drazinos, P., Gatos, I., Tsantis, S., Papadimitroulas, P., Spiliopoulos, S., Karnabatidis, D., Theotokas, I., Zoumpoulis, P., and Hazle, J.D. (2020). Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences. Phys. Med. Biol., 65.","DOI":"10.1088\/1361-6560\/abae06"},{"key":"ref_19","first-page":"246","article-title":"Decision tree classifier: A detailed survey","volume":"12","author":"Priyanka","year":"2020","journal-title":"Int. J. Inf. Decis. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"152","DOI":"10.21037\/atm.2019.03.29","article-title":"Predictive analytics with gradient boosting in clinical medicine","volume":"7","author":"Bao","year":"2019","journal-title":"Ann. Transl. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, W.M. (2019). Supervised Learning-Classification Using K-Nearest Neighbors (KNN). Python\u00ae Machine Learning, Wiley.","DOI":"10.1002\/9781119557500.ch9"},{"key":"ref_22","first-page":"11","article-title":"Review on classification based on artificial neural networks","volume":"2","author":"Saravanan","year":"2014","journal-title":"Int. J. Ambient Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"245","DOI":"10.21786\/bbrc\/13.14\/57","article-title":"A Detailed Review on Decision Tree and Random Forest","volume":"13","author":"Talekar","year":"2020","journal-title":"Biosci. Biotechnol. Res. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bisong, E. (2019). Support Vector Machines. Building Machine Learning and Deep Learning Models on Google Cloud Platform, Apress.","DOI":"10.1007\/978-1-4842-4470-8"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kornblith, S., Shlens, J., and Le, Q.V. (2019, January 15\u201320). Do Better ImageNet Models Transfer Better?. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00277"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Burri, S.R., Ahuja, S., Kumar, A., and Baliyan, A. (2023, January 5\u20136). Exploring the Effectiveness of Optimized Convolutional Neural Network in Transfer Learning for Image Classification: A Practical Approach. Proceedings of the 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India.","DOI":"10.1109\/InCACCT57535.2023.10141701"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2016). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2016, January 12\u201317). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_31","unstructured":"Terven, J.R., C\u00f3rdova-Esparza, D.M., Ram\u00edrez-Pedraza, A., and Chavez-Urbiola, E.A. (2023). Loss Functions and Metrics in Deep Learning. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Demirkaya, A., Chen, J., and Oymak, S. (2020, January 18\u201320). Exploring the Role of Loss Functions in Multiclass Classification. Proceedings of the 2020 54th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA.","DOI":"10.1109\/CISS48834.2020.1570627167"},{"key":"ref_33","unstructured":"Mukhoti, J., Kulharia, V., Sanyal, A., Golodetz, S., Torr, P.H.S., and Dokania, P.K. (2020, January 17\u201318). On using Focal Loss for Neural Network Calibration. Proceedings of the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning, Virtual."},{"key":"ref_34","unstructured":"Balles, L., and Hennig, P. (2017, January 6\u201311). Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_35","unstructured":"Achlioptas, P., and Stanford (2024, June 08). Stochastic Gradient Descent in Theory and Practice. Available online: https:\/\/api.semanticscholar.org\/CorpusID:96430121."},{"key":"ref_36","unstructured":"Dauphin, Y., de Vries, H., and Bengio, Y. (2015). RMSProp and equilibrated adaptive learning rates for non-convex optimization. arXiv."},{"key":"ref_37","unstructured":"Liu, B., Balaji, Y., Xue, L., and Min, M.R. (2020, January 30). Understanding Attention Mechanisms. Proceedings of the International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_38","unstructured":"Michel, P., Levy, O., and Neubig, G. (2019). Are Sixteen Heads Really Better than One?. arXiv."},{"key":"ref_39","unstructured":"TensorFlow (2024, June 08). Tf.keras.preprocessing.image.ImageDataGenerator. Available online: https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/keras\/preprocessing\/image\/ImageDataGenerator."},{"key":"ref_40","unstructured":"Albumentations (2024, June 08). Efficient Image Augmentation Library for Machine Learning. Available online: https:\/\/albumentations.ai\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/23\/7568\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:40:35Z","timestamp":1760114435000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/23\/7568"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":40,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["s24237568"],"URL":"https:\/\/doi.org\/10.3390\/s24237568","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]}}}