Traditional deep learning models have been extensively utilized in lung cancer computer-aided diagnosis (CAD) studies. These models typically treat false positive and false negative cases equally during training. However, in the specific context of lung nodule malignancy CAD studies, our objective is to improve sensitivity without significantly compromising overall accuracy. To address this, our study proposes the incorporation of cost values into the sigmoid activation function for deep learning-based CAD systems used in lung nodule malignancy classification. Through empirical analysis, we observed a significant 4% increase in sensitivity while effectively maintaining the overall accuracy. The results obtained from our study provide compelling evidence that incorporating cost values into the training scheme can significantly enhance sensitivity in the classification of lung nodule malignancy. Furthermore, we emphasize the importance of considering the cost values as hyperparameters in future CAD systems. By appropriately tuning these cost values, we can further optimize the performance and efficacy of lung nodule malignancy CAD systems.
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