  A common problem found in real-word medical image classification is the
inherent imbalance of the positive and negative patterns in the dataset where
positive patterns are usually rare. Moreover, in the classification of multiple
classes with neural network, a training pattern is treated as a positive
pattern in one output node and negative in all the remaining output nodes. In
this paper, the weights of a training pattern in the loss function are designed
based not only on the number of the training patterns in the class but also on
the different nodes where one of them treats this training pattern as positive
and the others treat it as negative. We propose a combined approach of weights
calculation algorithm for deep network training and the training optimization
from the state-of-the-art deep network architecture for thorax diseases
classification problem. Experimental results on the Chest X-Ray image dataset
demonstrate that this new weighting scheme improves classification
performances, also the training optimization from the EfficientNet improves the
performance furthermore. We compare the aggregate method with several
performances from the previous study of thorax diseases classifications to
provide the fair comparisons against the proposed method.
