Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images
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, wh...
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doaj-4a054dbdb90b41aaaba1fe1153c0c79b2020-11-25T02:53:45ZengMDPI AGSensors1424-82202020-05-01203085308510.3390/s20113085Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound ImagesRaluca Brehar0Delia-Alexandrina Mitrea1Flaviu Vancea2Tiberiu Marita3Sergiu Nedevschi4Monica Lupsor-Platon5Magda Rotaru6Radu Ioan Badea7Computer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, RomaniaComputer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, RomaniaComputer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, RomaniaComputer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, RomaniaComputer Science Department, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114 Cluj Napoca, RomaniaRegional Institute of Gastroenterology and Hepatology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, 19-21 Croitorilor Street, 400162 Cluj-Napoca, RomaniaIuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, 8 Babes Street, 400012 Cluj-Napoca, RomaniaRegional Institute of Gastroenterology and Hepatology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, 19-21 Croitorilor Street, 400162 Cluj-Napoca, RomaniaThe 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.https://www.mdpi.com/1424-8220/20/11/3085Image Processing, Convolutional Neural Networks (CNN), Pattern Recognition, Ultrasound Images, Hepatocellular Carcinoma (HCC), Automatic Diagnosis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Raluca Brehar Delia-Alexandrina Mitrea Flaviu Vancea Tiberiu Marita Sergiu Nedevschi Monica Lupsor-Platon Magda Rotaru Radu Ioan Badea |
spellingShingle |
Raluca Brehar Delia-Alexandrina Mitrea Flaviu Vancea Tiberiu Marita Sergiu Nedevschi Monica Lupsor-Platon Magda Rotaru Radu Ioan Badea Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images Sensors Image Processing, Convolutional Neural Networks (CNN), Pattern Recognition, Ultrasound Images, Hepatocellular Carcinoma (HCC), Automatic Diagnosis |
author_facet |
Raluca Brehar Delia-Alexandrina Mitrea Flaviu Vancea Tiberiu Marita Sergiu Nedevschi Monica Lupsor-Platon Magda Rotaru Radu Ioan Badea |
author_sort |
Raluca Brehar |
title |
Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images |
title_short |
Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images |
title_full |
Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images |
title_fullStr |
Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images |
title_full_unstemmed |
Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images |
title_sort |
comparison of deep-learning and conventional machine-learning methods for the automatic recognition of the hepatocellular carcinoma areas from ultrasound images |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-05-01 |
description |
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. |
topic |
Image Processing, Convolutional Neural Networks (CNN), Pattern Recognition, Ultrasound Images, Hepatocellular Carcinoma (HCC), Automatic Diagnosis |
url |
https://www.mdpi.com/1424-8220/20/11/3085 |
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