Sentence examples for pathology detection from inspiring English sources

The goal of this chapter is to give an overview of the research we have been conducting in automated X-ray pathology detection for the past 10 years, from bag-of-visual-words (BoVW) models to the Convolutional Neural Network CNN Deep Learning schemeses.

That is why, each language needs its special technique for vocal fold pathology detection system.

Characteristic parameters, such as Mel-frequency cepstral coefficients (MFCC), have also become more popular for voice pathology detection [6, 8, 10 12].

Of course our main aim is to develop a high-efficient method for vocal fold pathology detection based on Russian language.

For this reason, such recordings are widely used to support the diagnosis, making automatic decision systems important tools to improve the pathology detection and its evaluation.

As it can be seen in Additional file 1, another disadvantage of some of the previous works for the vocal fold pathology detection systems is that their reported classification accuracies are often about 90% or even less.

In such pathologies, detection of the pathological form of the prion protein (i.e., the causative factor) in blood is difficult and therefore identification of new biomarkers implicated in the pathway of prion infection is relevant.

However, among others, we think that both musculoskeletal and neurological radiologists tend to focus their attention mainly on spinal pathology when detection of extra-spinal findings needs recall of their general radiology training.

Molecular or biochemical biomarkers of joint metabolism offer promise in helping us understand joint pathology, its detection and treatment.

Given CFPHV's putative causal role in FP, to date most studies into the epidemiology, pathology, DNA detection, prevalence and phylogeography of CFPHV have been performed using DNA extracted from tumour tissue [ 8, 19, 24, 25], thus cannot be used to estimate the prevalence of latent CFPHV infections.

With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant.