… The data normalization process was also applied on all of … not influence the overall binary
classification dilemma. These pre-… binary classification issues. In Table 6, we also compare the …

… automatic classification of 12 abnormalities visible in chest X-rays. … , we propose a novel
normalization technique. With this method … We create D binary cross-entropy loss functions. The …

… [19] presented a technique for the binary classification of … on chest X-ray for the classification
of chest infections, including … using the classification head without batch normalization and …

… feature based binary classifier. The features are extracted from … -anterior (PA) chest X-ray
radiographs acquired from both … composed of a batch normalization layer and a convolutional …

… this study included intensity normalization, masking of obvious … that can perform binary group
separation. A SVM performs … textural characterisation of chest X-ray images for pneumonia …

… Following the ‘P1’ layer is the Batch Normalization layer, … of binary classification. The result
shows that the proposed approach offers a very high prediction accuracy on the chest X-ray …

… shortcut connections and batch normalization. The decoder … units [19] and a batch
normalization layer [20], respectively. … binary classification criteria to train the CNN to assess chest …

… of our approach over binary classification is that we avoid … relative probability and the
normalization scales the values into [0… on chest X-ray images and is superior to binary …

… Binary normality classification of chest radiographs may be valuable to assist clinicians
as a screening tool. Second, if the parameters of the DCNN are tuned to each type of …

… AM_DenseNet for chest X-ray image classification. The … each convolutional layer contains
Batch Normalization (BN), ReLU … -label classification loss can be translated into a binary class …