Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques

Mitrea, Delia-Alexandrina; Brehar, Raluca; Nedevschi, Sergiu; Lupsor-Platon, Monica; Socaciu, Mihai; Badea, Radu

doi:10.3390/s23052520

Open AccessEditor’s ChoiceArticle

Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques

by

Delia-Alexandrina Mitrea

^1,*

,

Raluca Brehar

¹

,

Sergiu Nedevschi

¹

,

Monica Lupsor-Platon

^2,3

,

Mihai Socaciu

^2,3 and

Radu Badea

^2,3

¹

Department of Computer Science, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania

²

Department of Medical Imaging, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania

³

“Prof. Dr. O. Fodor” Regional Institute of Gastroenterology and Hepatology, 400162 Cluj-Napoca, Romania

^*

Author to whom correspondence should be addressed.

Sensors 2023, 23(5), 2520; https://doi.org/10.3390/s23052520

Submission received: 31 December 2022 / Revised: 7 February 2023 / Accepted: 21 February 2023 / Published: 24 February 2023

(This article belongs to the Special Issue Deep Learning for Pathology Detection and Diagnosis in Medical Imaging)

Download

Browse Figures

Versions Notes

Abstract

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.

Keywords: convolutional neural networks (CNN); conventional machine learning (CML); advanced texture analysis methods; combination techniques; classification performance; hepatocellular carcinoma (HCC); ultrasound images

Share and Cite

MDPI and ACS Style

Mitrea, D.-A.; Brehar, R.; Nedevschi, S.; Lupsor-Platon, M.; Socaciu, M.; Badea, R. Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques. Sensors 2023, 23, 2520. https://doi.org/10.3390/s23052520

AMA Style

Mitrea D-A, Brehar R, Nedevschi S, Lupsor-Platon M, Socaciu M, Badea R. Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques. Sensors. 2023; 23(5):2520. https://doi.org/10.3390/s23052520

Chicago/Turabian Style

Mitrea, Delia-Alexandrina, Raluca Brehar, Sergiu Nedevschi, Monica Lupsor-Platon, Mihai Socaciu, and Radu Badea. 2023. "Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques" Sensors 23, no. 5: 2520. https://doi.org/10.3390/s23052520

APA Style

Mitrea, D.-A., Brehar, R., Nedevschi, S., Lupsor-Platon, M., Socaciu, M., & Badea, R. (2023). Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques. Sensors, 23(5), 2520. https://doi.org/10.3390/s23052520

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI