{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:03:11Z","timestamp":1782313391659,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T00:00:00Z","timestamp":1590710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Romanian National Authority for Scientific Research and Innovation","award":["PNIII-P1-1.2-PCCDI2017-0221 Nr.59\/1st March 2018"],"award-info":[{"award-number":["PNIII-P1-1.2-PCCDI2017-0221 Nr.59\/1st March 2018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.<\/jats:p>","DOI":"10.3390\/s20113085","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T09:19:27Z","timestamp":1591089567000},"page":"3085","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0978-7826","authenticated-orcid":false,"given":"Raluca","family":"Brehar","sequence":"first","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4293-922X","authenticated-orcid":false,"given":"Delia-Alexandrina","family":"Mitrea","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0328-8789","authenticated-orcid":false,"given":"Flaviu","family":"Vancea","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5987-9174","authenticated-orcid":false,"given":"Tiberiu","family":"Marita","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2018-4647","authenticated-orcid":false,"given":"Sergiu","family":"Nedevschi","sequence":"additional","affiliation":[{"name":"Computer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7918-1956","authenticated-orcid":false,"given":"Monica","family":"Lupsor-Platon","sequence":"additional","affiliation":[{"name":"Regional Institute of Gastroenterology and Hepatology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, 19-21 Croitorilor Street, 400162 Cluj-Napoca, Romania"},{"name":"Medical  Imaging Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, 8 Babes Street, 400012 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3160-5489","authenticated-orcid":false,"given":"Magda","family":"Rotaru","sequence":"additional","affiliation":[{"name":"Medical  Imaging Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, 8 Babes Street, 400012 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5330-090X","authenticated-orcid":false,"given":"Radu Ioan","family":"Badea","sequence":"additional","affiliation":[{"name":"Regional Institute of Gastroenterology and Hepatology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, 19-21 Croitorilor Street, 400162 Cluj-Napoca, Romania"},{"name":"Medical  Imaging Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, 8 Babes Street, 400012 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,29]]},"reference":[{"key":"ref_1","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. 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