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---
license: mit
language:
- en
tags:
- image classification
- classification
- medical imaging
- medical
- dicom
- cancer
metrics:
- '62% Sensitivity'
---
# HerBreastsFriend(HBF)
A model for identifying breast cancer in patients inspired by a study conducted by Duke & blogged about by jamanetwork[^1].
Their studies finding's were that there's a lot of room for improvement. They came to this conclusion after building their
own AI model for breast cancer detection/prognoses and achieved a 65% on sensitivity.
### Details
- KNN strategy
- n_neighbors=5
- StandardScaler
- PCA
- n_components=2
- Trained on limited dataset(1997 images)
- I had to limit the number of data points in my model because my machine kept freezing. WIP on a solution.
- Hosted by the amazing cancerimagingarchive[^2]
### Classification Report
The initial release of HBF scored the following in our classification. 62% for average weighted across all features. A lot of room for improvement.
```sh
precision recall f1-score support
Normal 0 0.62 0.80 0.70 956
Actionable 1 0.61 0.58 0.59 760
Benign 2 0.69 0.07 0.12 164
Cancer 3 0.47 0.08 0.13 117
accuracy 0.62 1997
macro avg 0.60 0.38 0.39 1997
weighted avg 0.61 0.62 0.58 1997
```
### FAQ
I'm considering making this open source. If you'd like to contribute please give a star to let me know there's others interested.
[^1] Duke Study https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2783046
[^2] [cancerimagingarchive https://www.breastcancer.org/facts-statistics](https://www.cancerimagingarchive.net/collection/breast-cancer-screening-dbt)
A study conducted by Duke University Health System assessed "deep learning" and "medical imaging in general" have significant advancements left to go in the future.
Their conclusion comes following thier own AI models, trained to detect cancer in a non-invasive way(requiring no biopsy), was evaluated at only 65% sensitivity.
Although in reality no easy feat, a disappointing statistic from the US's 7th best University.
I, having experience in the industry & seeking a meaningful project to work on, felt compelled to see what I could do to move the needle.
The result of this was creating a model which was evaluated at 62% using scikit-learn's classification report.
The model is hosted on Hugging Face linked below. And soon radiologist & patients will all be able to use this model for free(and future improved versions of it) at https://lnkd.in/eTWCD2wu
https://lnkd.in/eTx_sw9k |