Pancreatic Tumor Recognition from CT Images through Advanced Deep Learning Techniques.
Proceedings of the IEEE International Conference on Automation, 2024
Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques.
Sensors, March, 2023
Hepatocellular Carcinoma Recognition from Ultrasound Images by Fusing Convolutional Neural Networks at Decision Level.
Proceedings of the 46th International Conference on Telecommunications and Signal Processing, 2023
Liver Tumor Segmentation From Computed Tomography Images Through Convolutional Neural Networks.
Proceedings of the 9th International Conference on Systems and Informatics, 2023
Kidney Tumor Segmentation and Grade Identification in CT Images.
Proceedings of the 19th IEEE International Conference on Intelligent Computer Communication and Processing, 2023
Automatic Segmentation of Periodontal Tissue Ultrasound Images with Artificial Intelligence: A Novel Method for Improving Dataset Quality.
Sensors, 2022
Influence of Expert-Dependent Variability over the Performance of Noninvasive Fibrosis Assessment in Patients with Chronic Hepatitis C by Means of Texture Analysis.
Comput. Math. Methods Medicine, 2012
Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images.
Comput. Math. Methods Medicine, 2012
The Role of the Feature Extraction Methods in Improving the Textural Model of the Hepatocellular Carcinoma, Based on Ultrasound Images.
Proceedings of the Digital Information Processing and Communications, 2011
Texture analysis as a noninvasive tool for fibrosis assessment in chronic hepatitis C. influence of expert dependent variability over the performance of texture analysis.
Proceedings of the IEEE International Conference on Intelligent Computer Communication and Processing, 2011