{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T20:28:45Z","timestamp":1779308925834,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Research, Innovation and Digitization, CNCS\u2014UEFISCDI","award":["PN-III-P-1.1-TE-2021-1293"],"award-info":[{"award-number":["PN-III-P-1.1-TE-2021-1293"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor and the third cause of cancer-related deaths worldwide. For many years, the golden standard for HCC diagnosis has been the needle biopsy, which is invasive and carries risks. Computerized methods are due to achieve a noninvasive, accurate HCC detection process based on medical images. We developed image analysis and recognition methods to perform automatic and computer-aided diagnosis of HCC. Conventional approaches that combined advanced texture analysis, mainly based on Generalized Co-occurrence Matrices (GCM) with traditional classifiers, as well as deep learning approaches based on Convolutional Neural Networks (CNN) and Stacked Denoising Autoencoders (SAE), were involved in our research. The best accuracy of 91% was achieved for B-mode ultrasound images through CNN by our research group. In this work, we combined the classical approaches with CNN techniques, within B-mode ultrasound images. The combination was performed at the classifier level. The CNN features obtained at the output of various convolution layers were combined with powerful textural features, then supervised classifiers were employed. The experiments were conducted on two datasets, acquired with different ultrasound machines. The best performance, above 98%, overpassed our previous results, as well as representative state-of-the-art results.<\/jats:p>","DOI":"10.3390\/s23052520","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T03:01:01Z","timestamp":1677207661000},"page":"2520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4293-922X","authenticated-orcid":false,"given":"Delia-Alexandrina","family":"Mitrea","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0978-7826","authenticated-orcid":false,"given":"Raluca","family":"Brehar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2018-4647","authenticated-orcid":false,"given":"Sergiu","family":"Nedevschi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7918-1956","authenticated-orcid":false,"given":"Monica","family":"Lupsor-Platon","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania"},{"name":"\u201cProf. Dr. O. Fodor\u201d Regional Institute of Gastroenterology and Hepatology, 400162 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mihai","family":"Socaciu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania"},{"name":"\u201cProf. Dr. O. Fodor\u201d Regional Institute of Gastroenterology and Hepatology, 400162 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radu","family":"Badea","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania"},{"name":"\u201cProf. Dr. O. Fodor\u201d Regional Institute of Gastroenterology and Hepatology, 400162 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"European Association for the Study of the Liver, and European Organisation for Research and Treatment of Cancer (2012). EASL-EORTC clinical practice guidelines: Management of hepatocellular carcinoma. J. Hepatol., 56, 908\u2013943.","DOI":"10.1016\/j.jhep.2011.12.001"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s11894-005-0060-7","article-title":"Approaches to the diagnosis of hepatocellular carcinoma","volume":"7","author":"Sherman","year":"2005","journal-title":"Curr. Gastroenterol. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1002\/cld.991","article-title":"LI-RADS: Review and updates","volume":"7","author":"Elmohr","year":"2021","journal-title":"Clin. Liver Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"348135","DOI":"10.1155\/2012\/348135","article-title":"Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images","volume":"2012","author":"Mitrea","year":"2012","journal-title":"Comput. Math. Methods Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mitrea, D., Nedevschi, S., and Badea, R. (2018, January 16\u201318). Automatic Recognition of the Hepatocellular Carcinoma from Ultrasound Images using Complex Textural Microstructure Co-Occurrence Matrices (CTMCM). Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods\u2014-Volume 1: ICPRAM, INSTICC, Funchal, Portugal.","DOI":"10.5220\/0006652101780189"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Brehar, R., Mitrea, D.A., Vancea, F., Marita, T., Nedevschi, S., Lupsor-Platon, M., Rotaru, M., and Badea, R. (2020). Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images. Sensors, 20.","DOI":"10.3390\/s20113085"},{"key":"ref_7","unstructured":"Mitrea, D., Brehar, R., Nedevschi, S., Socaciu, M., and Badea, R. (2022, January 20\u201322). Hepatocellular Carcinoma recognition from ultrasound images through Convolutional Neural Networks and their combinations. Proceedings of the International Conference on Advancements of Medicine and Health Care through Technology, Cluj-Napoca, Romania. IFMBE Proceedings Series."},{"key":"ref_8","unstructured":"Mitrea, D., Mendoiu, C., Mitrea, P., Nedevschi, S., Lupsor-Platon, M., Rotaru, M., and Badea, R. (2020, January 12\u201315). HCC Recognition within B-mode and CEUS Images using Traditional and Deep Learning Techniques. Proceedings of the 7th International Conference on Advancements of Medicine and Health Care through Technology, Cluj-Napoca, Romania. IFMBE Proceedings Series."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1002\/jcu.1870130203","article-title":"Diagnostic accuracy of computerized B-scan texture analysis and conventional ultrasonography in diffuse parenchymal and malignant liver disease","volume":"13","author":"Raeth","year":"1985","journal-title":"J. Clin. Ultrasound"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1088\/0031-9155\/48\/22\/008","article-title":"Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images","volume":"48","author":"Yoshida","year":"2003","journal-title":"Phys. Med. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"49","DOI":"10.2478\/slgr-2013-0039","article-title":"Computer aided diagnosis of liver tumors based on multi-image texture analysis of contrast-enhanced CT. Selection of the most appropriate texture features","volume":"35","author":"Duda","year":"2013","journal-title":"Stud. Log. Gramm. Rhetor."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1136\/gutjnl-2018-316204","article-title":"Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: A prospective multicentre study","volume":"68","author":"Hui","year":"2019","journal-title":"Gut"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, X., Song, J., Wang, S., Zhao, J., and Chen, Y. (2017). Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification. Sensors, 17.","DOI":"10.3390\/s17010149"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Koutrintzes, D., Mathe, E., and Spyrou, E. (2022, January 3\u20135). Boosting the Performance of Deep Approaches through Fusion with Handcrafted Features. Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods, Online.","DOI":"10.5220\/0010982700003122"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1016\/S0301-5629(96)00144-5","article-title":"Application of Artificial Neural Networks for the classification of liver lesions by texture parameters","volume":"22","author":"Sujana","year":"1996","journal-title":"Ultrasound Med. Biol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"59","DOI":"10.4015\/S1016237204000104","article-title":"A study of ultrasonic liver images classification with artificial neural networks based on fractal geometry and multiresolution analysis","volume":"16","author":"Lee","year":"2004","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4990","DOI":"10.3389\/fonc.2021.762733","article-title":"Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma","volume":"11","author":"Feng","year":"2021","journal-title":"Front. Oncol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Vivanti, R., and Epbrat, A. (2015, January 9). Automatic liver tumor segmentation in follow-up CT studies using convolutional neural networks. Proceedings of the Patch-Based Methods in Medical Image Processing Workshop, Munich, Germany.","DOI":"10.1007\/978-3-319-28194-0_7"},{"key":"ref_19","unstructured":"Zhantao, C., Lixin, D., and Guowu, Y. (2017). Patch-Based Techniques in Medical Imaging, Springer. Lecture Notes in Computer Science."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6215085","DOI":"10.1155\/2016\/6215085","article-title":"Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images","volume":"2016","author":"Li","year":"2016","journal-title":"Comput. Math. Methods Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"82688","DOI":"10.1109\/ACCESS.2020.2990683","article-title":"A Temporal Sequence Dual-Branch Network for Classifying Hybrid Ultrasound Data of Breast Cancer","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pradhan, P., Kohler, K., Guo, S., Rosin, O., Popp, J., Niendorf, A., and Bocklitz, T. (2021, January 4\u20136). Data Fusion of Histological and Immunohistochemical Image Data for Breast Cancer Diagnostics using Transfer Learning. Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods, Online.","DOI":"10.5220\/0010225504950506"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7010438","DOI":"10.1155\/2021\/7010438","article-title":"Feature Fusion Based on Convolutional Neural Network for Breast Cancer Auxiliary Diagnosis","volume":"2021","author":"Cheng","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_24","first-page":"2653","article-title":"An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification","volume":"69","author":"Aziz","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tian, Y., Komolafe, T.E., Zheng, J., Zhou, G., Chen, T., Zhou, B., and Yang, X. (2021). Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features. Diagnostics, 11.","DOI":"10.3390\/diagnostics11101875"},{"key":"ref_26","first-page":"011021","article-title":"Combining Many-objective Radiomics and 3-dimensional Convolutional Neural Network through Evidential Reasoning to Predict Lymph Node Metastasis in Head and Neck Cancer","volume":"5","author":"Chen","year":"2021","journal-title":"J. Med. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"011021","DOI":"10.1117\/1.JMI.5.1.011021","article-title":"Predicting malignant nodules by fusing deep features with classical radiomics features","volume":"5","author":"Paul","year":"2018","journal-title":"J. Med. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"388","DOI":"10.18383\/j.tom.2016.00211","article-title":"Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma","volume":"2","author":"Paul","year":"2016","journal-title":"Tomography"},{"key":"ref_29","unstructured":"Dutta, A., Gupta, A., and Zissermann, A. (2020, March 10). VGG Image Annotator (VIA). Version 2.0.9. Available online: http:\/\/www.robots.ox.ac.uk\/vgg\/software\/via\/."},{"key":"ref_30","unstructured":"Chatterjee, H.S. (2022, July 15). Various Types of Convolutional Neural Network. Available online: https:\/\/towardsdatascience.com\/various-types-of-convolutional-neural-network-8b00c9a08a1b."},{"key":"ref_31","unstructured":"(2015). Tutorial of Deep Learning, University of Montreal. Release 0.1."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (2016). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_36","unstructured":"Tan, M., and Le, Q. (2020). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv."},{"key":"ref_37","unstructured":"(2022, April 19). Torchvision Library for Python. Available online: https:\/\/pytorch.org\/vision\/stable\/index.html."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, Z., Gu, T., Li, B., Xu, W., He, X., and Hui, X. (2022). ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model. Appl. Sci., 12.","DOI":"10.3390\/app12189016"},{"key":"ref_39","unstructured":"Materka, A., and Strzelecki, M. (1998). Texture Analysis Methods\u2014A Review, Institute of Electronics, Technical University of Lodz. Technical Report."},{"key":"ref_40","unstructured":"Meyer-Base, A. (2009). Pattern Recognition for Medical Imaging, Elsevier."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","article-title":"A comparative study of texture measures with classification based on featured distributions","volume":"29","author":"Ojala","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TKDE.2003.1245283","article-title":"Benchmarking attribute selection techniques for discrete class data mining","volume":"15","author":"Hall","year":"2003","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_43","unstructured":"(2022, May 10). Waikato Environment for Knowledge Analysis (Weka 3). Available online: http:\/\/www.cs.waikato.ac.nz\/ml\/weka\/."},{"key":"ref_44","unstructured":"Gaber, T., and Hassanien, T. (2017). Particle Swarm Optimization: A Tutorial, IGI Global."},{"key":"ref_45","first-page":"66","article-title":"Dimensionality reduction: A comparative review","volume":"10","author":"Postma","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_46","unstructured":"(2022, April 14). Deep Learning Toolbox for Matlab. Available online: https:\/\/it.mathworks.com\/help\/deeplearning\/index.html."},{"key":"ref_47","unstructured":"Kitayama, M. (2020, August 20). Matlab-Kernel-PCA Toolbox. Available online: https:\/\/it.mathworks.com\/matlabcentral\/fileexchange\/71647-matlab-kernel-pca."},{"key":"ref_48","unstructured":"Too, J. (2022, April 03). Particle Swarm Optimization for Feature Selection. Available online: https:\/\/github.com\/JingweiToo\/-Particle-Swarm-Optimization-for-Feature-Selection."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1109\/TIP.2017.2765820","article-title":"Discriminative Multiple Canonical Correlation Analysis for Information Fusion","volume":"27","author":"Gao","year":"2018","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2520\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:41:17Z","timestamp":1760121677000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,24]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23052520"],"URL":"https:\/\/doi.org\/10.3390\/s23052520","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,24]]}}}