Journal Article

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

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

2023

Overview

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.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / advanced texture analysis methods / Biopsy / Carcinoma, Hepatocellular / classification performance / combination techniques / conventional machine learning (CML) / convolutional neural networks (CNN) / Datasets / Deep Learning / Diagnosis / Hepatitis / hepatocellular carcinoma (HCC) / Hepatoma / Humans / Image processing / Liver cancer / Liver cirrhosis / Liver Neoplasms - pathology / Machine learning / Magnetic resonance imaging / Medical imaging / Neural networks / Neural Networks, Computer / Support vector machines / Tumors / Ultrasonic imaging / Ultrasonography - methods / Ultrasound imaging / Wavelet transforms