Medical image modality classification using discrete Bayesian networks

Abstract

In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensive pipeline starting with feature extraction from images, data combination, pre-processing and a range of different classification techniques and models. We study the expressive power of several image descriptors along with supervised discretization and feature selection to show the performance of discrete Bayesian networks compared to the usual deterministic classifier used in image classification. We perform an exhaustive experimentation by using the ImageCLEFmed 2013 collection. This problem presents a high number of classes so we propose several hierarchical approaches. In a first set of experiments we evaluate a wide range of parameters for our pipeline along with several classification models. Finally, we perform a comparison by setting up the competition environment between our selected approaches and the best ones of the original competition. Results show that the Bayesian Network classifiers obtain very competitive results. Furthermore, the proposed approach is stable and it can be applied to other problems that present inherent hierarchical structures of classes.

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

“…BNs have been extensively used in different areas of research such as in the chemical mode of action classification for aquatic toxicology (Carriger et al, 2016) and ecological risk assessment (Lee & Lee, 2006;Pollino et al, 2007); to classify images in medical image analysis (Arias et al, 2016); to predict food fraud ; to detect surgical site infections and safety assessment of natural gas stations (Sohn et al, 2016;Zarei et al, 2016).…”

Prediction of COVID-19 Using Genetic Deep Learning Convolutional Neural Network (GDCNN)

“…Class imbalance distribution problems have been faced by most researchers whenever, they deal with the real datasets [16,17]. However, classifier focus on minimizing the global error rate, thus the algorithm concentrates on the majority classes, but, it also focuses on minority classes based on the problem domain such as medical image classification and credit card fraud detection [18,19]. In the real world, classifying pneumonia type using CXR images is also considered as imbalanced learning as there are only a few people with affected pneumonia than considering health persons [20,21].…”

“…The selection is based on the highest fitness value obtained by each individual. Only those with higher fitness ranking guarantees highest fitness value using elitism roulette wheel selection scheme shown in equation (18) and code length using equation (19). Furthermore, after crossover operation the number of layers for DCNN required is 9 and 10 respectively, which is shown in equation 22and 23 .…”

Applicability of a food chain analysis on aquaculture of Atlantic salmon to identify and monitor vulnerabilities and drivers of change for the identification of emerging risks

“…BNs have been extensively used in different areas of research such as in the chemical mode of action classification for aquatic toxicology (Carriger et al, 2016) and ecological risk assessment (Lee and Lee, 2006;Pollino et al, 2007); to classify images in medical image analysis (Arias et al, 2016); to predict food fraud (Bouzembrak and Marvin, 2016;Marvin et al, 2016a); to assess risk of nanomaterials (Money et al, 2014;Winkler et al, 2014;Linkov et al, 2015;Low-Kam et al, 2015); to detect surgical site infections and safety assessment of natural gas stations (Sohn et al, 2017;Zarei et al, 2017).…”