In this study, we trained a convolutional neural network (CNN) utilizing a mix of recent CNN architectural design strategies. Our goals are to leverage these modern techniques to improve the binary classification of kidney tumor images obtained using Multi-Photon Microscopy (MPM). We demonstrate that incorporating these newer model design elements, coupled with transfer learning, image standardization, and data augmentation, leads to significantly increased classification performance over previous results. Our best model averages over 90% sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUROC) in image-level classification across cross-validation folds, superior to the previous best in all four metrics.
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