Our goal was to establish objective 3D deep learning models that differentiate cerebral alterations based on the effect of chemotherapy and to visualize the pattern that was recognized by our model. The average performance of SE-ResNet-50 models was accuracy of 80%, precision of 78%, and 70% recall, and the SE-DenseNet-121 model reached identical results with an average 80% accuracy, 86% precision, and 80% recall. The regions with the greatest contributions highlighted by the integrated gradients algorithm for differentiating chemo-brain were default mode and dorsal attention networks. We hope these results will be helpful in clinically tracking chemo-brain in the future.
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