# Impact of H&E Stain Normalization on Deep Learning Models in Cancer Image Classification: Performance, Complexity, and Trade-Offs

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/impact-of-h-and-amp-e-stain-normalization-on-deep-learning-models-in-cancer/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Nuwan Madusanka,Pramudini Jayalath,Dileepa Fernando,S.L.P. Yasakethu,Byeong-Il Lee |
| Citations | 20 |
| DOI | 10.3390/cancers15164144 |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.mdpi.com/2072-6694/15/16/4144/pdf?version=1692253873 |
| OpenAlex ID | https://openalex.org/W4385949249 |
| PMID | 37627172 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Nuwan Madusanka](https://scholariq.org/researchers/nuwan-madusanka/)

## Paper journal

- [Cancers](https://scholariq.org/journals/cancers/)

## Paper primary topic

- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)

## Paper topics

- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Digital Imaging for Blood Diseases](https://scholariq.org/topics/digital-imaging-for-blood-diseases/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.