Automatic detection of pneumonia analyzing ultrasound digital images - Barrientos, R.; Roman-Gonzalez, A.; Barrientos-Porras, F.; Solis-Vasquez, L.; Correa, M.; Pajuelo, M.; Anticona, C.; Lavarello Montero, R.; Castañeda, B.; Oberhelman, R.; Checkley, W.; Gilman, R.H.; et al. (Institute of Electrical and Electronics Engineers, 2016)
Pneumonia is one of the major causes of child mortality. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. This paper presents a method for automatic diagnostics of pneumonia using ultrasound imaging of the lungs. The approach presented here is based on the analysis of patterns present in rectangular segments from the ultrasound digital images. Specific features from the characteristic vectors were obtained and classified with standard neural networks. A training and testing set of positive and negative vectors were compiled. Vectors obtained from a single patient were included only in the testing or in the training set, but never in both. Our approach was able to correctly classify vectors with evidence of pneumonia, with 91.5% sensitivity and 100% specificity.
Keywords: Pneumonia; Lung ultrasound; Ultrasound; Computer science; Artificial intelligence; Ultrasound imaging; Medical imaging; Set (abstract data type); Radiology; Pattern recognition (psychology); Medicine; Computer vision; Internal medicine; https://purl.org/pe-repo/ocde/ford#3.02.02