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Fractal Analysis of Elastographic Images for Automatic Detection of Diffuse Diseases of Salivary Glands: Preliminary Results.

, , , , , , , and . Comput. Math. Methods Medicine, (2013)

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HCC Recognition Within Ultrasound Images Employing Advanced Textural Features with Deep Learning Techniques., , , , and . CISP-BMEI, page 1-6. IEEE, (2019)Fractal Analysis of Elastographic Images for Automatic Detection of Diffuse Diseases of Salivary Glands: Preliminary Results., , , , , , , and . Comput. Math. Methods Medicine, (2013)Hepatocellular Carcinoma Recognition in Ultrasound Images Using Textural Descriptors and Classical Machine Learning., , , , , and . ICCP, page 491-497. IEEE, (2019)Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images., , , , , , , and . Sensors, 20 (11): 3085 (2020)Advanced Texture Analysis Techniques for Building Textural Models, with Applications in the Study of the Pathology Evolution Stages, based on Ultrasound Images., , , and . EPS, SciTePress, (2016)Deep convolutional neural nets for objective steatosis detection from liver samples., , , and . ICCP, page 385-390. IEEE, (2017)The role of the cooccurrence matrix based on complex extended microstructures in discovering the cirrhosis severity grades within US images., , , , and . CISP-BMEI, page 1-6. IEEE, (2017)Discovering the cirrhosis grades from ultrasound images by using textural features and clustering methods., , , and . TSP, page 633-637. IEEE, (2013)Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques., , , , , and . Sensors, 23 (5): 2520 (March 2023)