  Computer-aided diagnosis establishes methods for robust assessment of medical
image-based examination. Image processing introduced a promising strategy to
facilitate disease classification and detection while diminishing unnecessary
expenses. In this paper, we propose a novel metastatic cancer image
classification model based on DenseNet Block, which can effectively identify
metastatic cancer in small image patches taken from larger digital pathology
scans. We evaluate the proposed approach to the slightly modified version of
the PatchCamelyon (PCam) benchmark dataset. The dataset is the slightly
modified version of the PatchCamelyon (PCam) benchmark dataset provided by
Kaggle competition, which packs the clinically-relevant task of metastasis
detection into a straight-forward binary image classification task. The
experiments indicated that our model outperformed other classical methods like
Resnet34, Vgg19. Moreover, we also conducted data augmentation experiment and
study the relationship between Batches processed and loss value during the
training and validation process.
