# Cancer image classification based on DenseNet model

arXiv: https://arxiv.org/abs/2011.11186 · HTML: https://mlsanity.com/paper/2011.11186/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Transformation Invariant Cancerous Tissue Classification Using Spatially Transformed DenseNet](https://mlsanity.com/paper/2204.11066/) (2022)
- [Evaluating the Reliability of CNN Models on Classifying Traffic and Road Signs using LIME](https://mlsanity.com/paper/2309.05747/) (2023)
- [AI-Enabled Waste Classification as a Data-Driven Decision Support Tool for Circular Economy and Urban Sustainability](https://mlsanity.com/paper/2601.22418/) (2026)

## Abstract (arXiv)

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.