Scale-Invariance-Based Pre-Processing Drastically Improves Neural Network Learning: Case Study of Diagnosing Lung Dysfunction in Children

Authors

Nancy Avila, The University of Texas at El PasoFollow
Julio Urenda, The University of Texas at El PasoFollow
Nelly Gordillo, Universidad Autónoma de Ciudad JuárezFollow
Vladik Kreinovich, The University of Texas at El PasoFollow

Publication Date

3-2019

Comments

Technical Report: UTEP-CS-19-19

Abstract

To adequately treat different types of lung dysfunctions in children, it is important to properly diagnose the corresponding dysfunction, and this is not an easy task. Neural networks have been trained to perform this diagnosis, but they are not perfect in diagnostics: their success rate is 60%. In this paper, we show that by selecting an appropriate invariance-based pre-processing, we can drastically improve the diagnostic success, to 100% for diagnosing the presence of a lung dysfunction.