Cancer image classification based on DenseNet model
This work addresses the problem of metastatic cancer detection in medical images, which is relevant for pathologists and medical diagnosis, offering an incremental improvement over existing methods.
This paper proposes a DenseNet-based model for classifying metastatic cancer in small image patches from digital pathology scans. The model was evaluated on a modified PatchCamelyon (PCam) dataset and reportedly outperformed ResNet34 and VGG19.
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.