A comparison of texture features versus deep learning for image classification in interstitial lung disease

Abstract

Interstitial lung disease (ILD) is a multifactorial condition that is difficult to diagnose. High-resolution computed tomography (CT) is commonly the imaging modality of choice, as it enables detection and mapping of distinctive pathological patterns. The distribution of these patterns gives clues as to the correct histological diagnosis. This paper compares two approaches to detecting these complex patterns: “man-made” features, based on classical handcrafted texture descriptors, and “machine-made” features, built with deep learning convolutional neural networks (CNNs). The two paradigms are evaluated on scans from 132 subjects, derived from two public databases of high resolution ILD CT images and associated expert annotations. Five specific tissue patterns are included: healthy, emphysema, fibrosis, ground glass opacity, and micronodules. The subjects are divided into training, validation and test groups. On the validation data the best handcrafted solution achieves a class assignment accuracy of 76.0%, compared with the best deep learning accuracy of 79.0%. For the test group, which was not used during development and only tested once, the handcrafted method achieves 65.5%, compared with the CNN accuracy of 69.9%. The results indicate that deep learning CNNs can outperform traditional texture measures, even on a low-level texture classification task such as this.