Multiclass Classification of Hepatic Anomalies with Dielectric Properties: From Phantom Materials to Rat Hepatic Tissues Openended coaxial probes can be used as tissue characterization devices. However, the technique suffers from a high error rate. To improve this technology, there is a need to decrease the measurement error which is reported to be more than 30% for an in vivo measurement setting. This work investigates the machine learning (ML) algorithmsxe2x80x99 ability to decrease the measurement error of openended coaxial probe techniques to enable tissue characterization devices. To explore the potential of this technique as a tissue characterization device, performances of multiclass ML algorithms on collected in vivo rat hepatic tissue and phantom dielectric property data were evaluated. Phantoms were used for investigating the potential of proliferating the data set due to difficulty of in vivo data collection from tissues. The dielectric property measurements were collected from 16 rats with hepatic anomalies, 8 rats with healthy hepatic tissues, and in house phantoms. Three ML algorithms, knearest neighbors (kNN), logistic regression (LR), and random forests (RF) were used to classify the collected data. The best performance for the classification of hepatic tissues was obtained with 76% accuracy using the LR algorithm. The LR algorithm performed classification with over 98% accuracy within the phantom data and the model generalized to in vivo dielectric property data with 48% accuracy. These findings indicate first, linear models, such as logistic regression, perform better on dielectric property data sets. Second, ML models fitted to the data collected from phantom materials can partly generalize to in vivo dielectric property data due to the discrepancy between dielectric property variability.

Hepatocellular Carcinoma Recognition in Ultrasound Images Using Textural Descriptors and Classical Machine Learning The classification of the liver tissue based on the analysis of the ultrasound images is an important task in the field of computer aided diagnosis, the ultrasonography providing a noninvasive, lowcost, imaging solution. An early, accurate diagnosis of the malignant tumours, based on those images, would be very useful for both patients and doctors. Within ultrasound images, the malignant tumours can be hardly distinguished by the human eye, the golden standard for diagnosis being the biopsy, an invasive, dangerous method. In our research, we develop computerized, noninvasive methods, based on advanced image analysis and recognition techniques, for performing tumour detection within ultrasound images. This paper proposes a method for hepatocellular carcinoma recognition in ultrasound images, by employing textural features obtained from the Gray Level Cooccurence Matrix (GLCM), combined with Local Binary Patterns (LBP). At the end, a true positive rate of about 72% was obtained for HCC, using ensembles of Adaptive Boosted classifiers.