Evaluation of Deep Feedforward Neural Networks for Classification of Diffuse Lung Diseases

Abstract

Diffuse Lung Diseases (DLDs) are a challenge for physicians due their wide variety. Computer-Aided Diagnosis (CAD) are systems able to help physicians in their diagnoses combining information provided by experts with Machine Learning (ML) methods. Among ML techniques, Deep Learning has recently established itself as one of the preferred methods with state-of-the-art performance in several fields. In this paper, we analyze the discriminatory power of Deep Feedforward Neural Networks (DFNN) when applied to DLDs. We classify six radiographic patterns related with DLDs: pulmonary consolidation, emphysematous areas, septal thickening, honeycomb, ground-glass opacities, and normal lung tissues. We analyze DFNN and other ML methods to compare their performance. The obtained results show the high performance obtained by DFNN method, with an overall accuracy of 99.60%, about 10% higher than the other studied ML methods.