… using small batches with a binary cross entropy loss function. … our classification accuracy
on the chest X-ray dataset we … Furthermore, we observe that the CNN with batch normalization …

… a CADx system for binary classification of chest X-ray images into two … normalization layer
can stabilize the learning process and reach convergence faster [32]. The batch normalization …

… of CNNs for the binary classification of two-view chest radiographs. The combination of extra
… Smaller batch sizes lead to larger generalization error when using batch normalization [23], …

… the challenges of BL classification in chest X-ray images with … and a heavy use of batch
normalization. Gao Huangand et al. … by applying the binary classifier with binary cross entropy as …

… normalization, dynamic dropout, learning rate decay, L2 regularization weight decay along
with Adam optimizer and binary … for classification of thoracic disease in chest radiography. …

… Using a binary classification system involving different … layer, both batch normalization and
ReLU activation function were … of COVID-19 in a chest X-ray image and distinguish it from …

… This study proposes MAN framework for the classification of … DL frameworks to examine
chest X-Rays and the lung CTs. … consists of convolution, ReLU, normalization, and max-pooling …

… , batch normalization, dropout, and classification layers combined … chest X-ray images.
The main target of our proposed system is to provide accurate detection for binary classification …

… normalization (BN) values including γ and β from the viewpoint of transfer learning and found
that appropriate initialization at all binary … in CXR to classify lesions are Chest X-Ray 14 (…

… 19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in
three zones and laterally, a histogram normalization is … image and then classify binary with an …