title: A comparative study for chest radiograph image retrieval using binary texture and deep learning classification.
abstract: In this work various approaches are investigated for X-ray image retrieval and specifically chest pathology retrieval. Given a query image taken from a data set of 443 images, the objective is to rank images according to similarity. Different features, including binary features, texture features, and deep learning (CNN) features are examined. In addition, two approaches are investigated for the retrieval task. One approach is based on the distance of image descriptors using the above features (hereon termed the "descriptor"-based approach); the second approach ("classification"-based approach) is based on a probability descriptor, generated by a pair-wise classification of each two classes (pathologies) and their decision values using an SVM classifier. Best results are achieved using deep learning features in a classification scheme.
authors.forename: Yaron
authors.initials: Y
authors.lastname: Anavi
authors.forename: Ilya
authors.initials: I
authors.lastname: Kogan
authors.forename: Elad
authors.initials: E
authors.lastname: Gelbart
authors.forename: Ofer
authors.initials: O
authors.lastname: Geva
authors.forename: Hayit
authors.initials: H
authors.lastname: Greenspan
mesh_terms: D000069550:Machine Learning; D013909:Thorax / Q000000981:diagnostic imaging*