… 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 …

… 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 …

… chest x-ray images. This architecture treats the problem as fine-grained binary classification
… , spatial drop-out, and group normalization to maximize the generalization capability. To …

… batch normalization … classification of all findings, regardless of their size, highlighting the
advantages of this approach. Conclusions:Different chest X-ray findings are better classified at …

… path was introduced as a binary hyperparameter. We used batch normalization [21] after
every convolution layer as it improved performance by enabling more efficient learning. …

… chest X-ray classification. We restrict the comparisons between the algorithms and radiologists
to image-based classification… networks in chest radiograph binary normality classification. …

… Each of the classifiers in the proposed approach performed binary classification. In the first
… These features were normalized in the range between 0 and 1 with min–max normalization. …