Improving computer-aided diagnosis of interstitial disease in chest radiographs by combining one-class and two-class classifiers.

In this paper we compare and combine two distinct pattern classification approaches to the automated detection of regions with interstitial abnormalities in frontal chest radiographs. Standard two-class classifiers and recently developed one-class classifiers are considered. The one-class problem is to find the best model of the normal class and reject all objects that don’t fit the model of normality. This one-class methodology was developed to deal with poorly balanced classes, and it uses only objects from a well-sampled class for training. This may be an advantageous approach in medical applications, where normal examples are easier to obtain than abnormal cases. We used receiver operating characteristic (ROC) analysis to evaluate classification performance by the different methods as a function of the number of abnormal cases available for training. Various two-class classifiers performed excellently in case that enough abnormal examples were available (area under ROC curve Az = 0.985 for a linear discriminant classifier). The one-class approach gave worse result when used stand-alone (Az = 0.88 for Gaussian data description) but the combination of both approaches, using a mean combining classifier resulted in better performance when only few abnormal samples were available (average Az = 0.94 for the combination and Az = 0.91 for the stand-alone linear discriminant in the same set-up). This indicates that computer-aided diagnosis schemes may benefit from using a combination of two-class and one-class approaches when only few abnormal samples are available.Information and Communication Theory GroupElectrical Engineering, Mathematics and Computer Scienc

A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography

Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of artworks while avoiding the loss of any precious materials that make them up. The use of Infrared Thermography is an interesting concept since surface and subsurface faults can be discovered by utilizing the 3D diffusion inside the object caused by external heat. The primary goal of this research is to detect defects in artworks, which is one of the most important tasks in the restoration of mural paintings. To this end, machine learning and deep learning techniques are effective tools that should be employed properly in accordance with the experiment’s nature and the collected data. Considering both the temporal and spatial perspectives of step-heating thermography, a spatiotemporal deep neural network is developed for defect identification in a mock-up reproducing an artwork. The results are then compared with those of other conventional algorithms, demonstrating that the proposed approach outperforms the others.Structural Integrity & CompositesPattern Recognition and Bioinformatic