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The study demonstrated that CapsNet achieved the best discriminative performance (accuracy 81.3%, specificity 80.7%, sensitivity 82.2%) although its area under the curve was just marginally better than that of the optimal random forest (RF) based radiomics model. Not surprisingly, the performance of the CNN was only comparable to the other radiomics models. This study demonstrated that CapsNet is a viable potential framework for discriminating the subtypes of NSCLC, and its use could be extended to the recognition of other diseases especially in lim

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