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Automatic Recognition of the Hepatocellular Carcinoma from Ultrasound Images using Complex Textural Microstructure Co-Occurrence Matrices (CTMCM).

, , and . ICPRAM, page 178-189. SciTePress, (2018)

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Colorectal cancer recognition from ultrasound images, using complex textural microstructure cooccurrence matrices, based on Laws' features., , , and . TSP, page 458-462. IEEE, (2015)The Role of the Complex Extended Textural Microstructure Co-occurrence Matrix in the Unsupervised Detection of the HCC Evolution Phases, based on Ultrasound Images., , and . ICPRAM, page 698-705. SciTePress, (2016)Hepatocellular Carcinoma Segmentation within Ultrasound Images using Convolutional Neural Networks., , , , , and . ICCP, page 483-490. IEEE, (2019)Diseased tissue area detection and delimitation, by fusion between finite difference methods and textural analysis., , , , and . AQTR, page 1-5. IEEE, (2014)Hepatocellular Carcinoma Recognition from Ultrasound Images by Fusing Convolutional Neural Networks at Decision Level., , , , , and . TSP, page 238-243. IEEE, (2023)Liver Tumor Segmentation From Computed Tomography Images Through Convolutional Neural Networks., , , , , , and . ICSAI, page 1-6. IEEE, (2023)Manufacturing Execution System Specific Data Analysis-Use Case With a Cobot., and . IEEE Access, (2018)Automatic Recognition of the Hepatocellular Carcinoma from Ultrasound Images using Complex Textural Microstructure Co-Occurrence Matrices (CTMCM)., , and . ICPRAM, page 178-189. SciTePress, (2018)The Role of the Feature Extraction Methods in Improving the Textural Model of the Hepatocellular Carcinoma, Based on Ultrasound Images., , , and . ICDIPC (1), volume 188 of Communications in Computer and Information Science, page 496-509. Springer, (2011)Discovering the cirrhosis grades from ultrasound images by using textural features and clustering methods., , , and . TSP, page 633-637. IEEE, (2013)