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Cancer image classification based on DenseNet model
| Content Provider | Scilit |
|---|---|
| Author | Zhong, Ziliang Zheng, Muhang Mai, Huafeng Zhao, Jianan Liu, Xinyi |
| Copyright Year | 2020 |
| Description | Journal: Journal of Physics: Conference Series 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. |
| Related Links | https://iopscience.iop.org/article/10.1088/1742-6596/1651/1/012143/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/1651/1/012143 |
| Journal | Journal of Physics: Conference Series |
| Issue Number | 1 |
| Volume Number | 1651 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2020-11-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Interdisciplinary Mathematics Image Classification |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
