Subject(s)
COVID-19/epidemiology , Hospitalization/trends , Neoplasms/mortality , Pandemics , SARS-CoV-2 , Biopsy , Brazil/epidemiology , Colonoscopy , Hospital Mortality , Humans , Mammography , Neoplasms/diagnosis , Neoplasms/therapyABSTRACT
This corrects the article DOI: 10.1103/PhysRevE.87.043304.
ABSTRACT
We describe an algorithm for simulating ultrasound propagation in random one-dimensional media, mimicking different microstructures by choosing physical properties such as domain sizes and mass densities from probability distributions. By combining a detrended fluctuation analysis (DFA) of the simulated ultrasound signals with tools from the pattern-recognition literature, we build a Gaussian classifier which is able to associate each ultrasound signal with its corresponding microstructure with a very high success rate. Furthermore, we also show that DFA data can be used to train a multilayer perceptron which estimates numerical values of physical properties associated with distinct microstructures.
