… In conclusion, this study sought to apply machine learning to the binary normality classification
of plain film chest radiographs, in order to assist clinicians in the prioritisation of formal …

… method to detect normality/pathology in chest radiographs, which constitutes the … binary
patterns (LBP) to these areas. LBP histograms are then used as input features for a classification …

… thoracic diagnosis prediction with chest radiographs (15), … to automated classification of
chest radiographs as normal or … analysis problem to a binary triage classification task will lead …

… normalization strategy. Experiments were performed on an extensive collection of 297,541
chest radiographs … We create D binary cross-entropy loss functions. The corresponding labels …

… binary classification task, on the testing radiographs sourced from the same institution as the
training chest radiographs… for radiologists on average for 1344 chest X-rays). Additionally, in …

… the performance of a DL algorithm to detect normality in adult CXRs as a rule out test for … ,
when the labelling was treated as a binary classification problem (normal versus any abnormal …

… In order to avoid binary classification the empirical distribution over labels in the k-th
neighborhood is computed by normalizing the counts for each class leading to a probabilistic …

… only normality (similar to semi-supervised classification or … semi-supervised binary
classification performance using the … The normal and abnormal chest X-ray classification results in …

… In this work we evaluated a system for chest X-ray image retrieval using various … and
classification-based approaches). Earlier work (eg [4]) has shown that using an initial classification …

… In [9] a set of local binary pattern features is described that is gray-scale invariant. A more …
However, our pixel classification based lung segmentation technique is still among the top …