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Deep Convolutional Neural Networks (DCNNs) have attracted extensive attentionand been applied in many areas, including medical image analysis and clinicaldiagnosis. One major challenge is to conceive a DCNN model with remarkableperformance on both internal and external data. We demonstrate that DCNNs maynot generalize to new data, but increasing the quality and heterogeneity of thetraining data helps to improve the generalizibility factor. We useInceptionResNetV2 and DenseNet121 architectures to predict the risk of 5 commonchest pathologies. The experiments were conducted on three publicly availabledatabases: CheXpert, ChestX-ray14, and MIMIC Chest Xray JPG. The results showthe internal performance of each of the 5 pathologies outperformed externalperformance on both of the models. Moreover, our strategy of exposing themodels to a mix of different datasets during the training phase helps toimprove model performance on the external dataset.

Generalization of Deep Convolutional Neural Networks -- A Case-study on Open-source Chest Radiographs