Automatic classification of pulmonary diseases using a structural co-occurrence matrix

Abstract

The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be the third major cause of death worldwide in 2030. The identification of diseases using medical image processing techniques is in high demand to assist medical doctors to make more accurate diagnoses. However, although these techniques contribute in making medical diagnoses, most of them still need to have some parameters set and this can be a difficult and tedious process. In this paper, a new automatic approach to identify and classify lung diseases from a structural co-occurrence matrix (SCM) in chest computed tomography images is proposed. The most important novelty of this approach is that only the image is used as the input data and extract the structural information of the disease which, in this case, is related to the lower frequencies. In order to demonstrate the efficiency of the proposed technique, it was compared with other well-known state-of-art feature extractors. In addition, the SCM was evaluated with four filters (Gaussian, Fourier, Laplace and Sobel) using linear discriminant analysis, multi-layer perceptron, support vector machines and minimal learning machine classifiers. The results showed that the SCM, when using low frequencies, is able to adapt to different images and extract the most significant structural data, without the need of any additional parameters, yet maintaining the diagnostic precision.