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---
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license: mit
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language:
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- en
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tags:
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- image classification
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- classification
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- medical imaging
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- medical
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- dicom
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- cancer
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metrics:
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- '62% Sensitivity'
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---
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# HerBreastsFriend(HBF)
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A model for identifying breast cancer in patients inspired by a study conducted by Duke & blogged about by jamanetwork[^1].
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Their studies finding's were that there's a lot of room for improvement. They came to this conclusion after building their
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own AI model for breast cancer detection/prognoses and achieved a 65% on sensitivity.
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### Details
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- KNN strategy
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- n_neighbors=5
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- StandardScaler
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- PCA
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- n_components=2
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- Trained on limited dataset(1997 images)
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- I had to limit the number of data points in my model because my machine kept freezing. WIP on a solution.
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- Hosted by the amazing cancerimagingarchive[^2]
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### Classification Report
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The initial release of HBF scored the following in our classification. 62% for average weighted across all features. A lot of room for improvement.
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```sh
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precision recall f1-score support
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Normal 0 0.62 0.80 0.70 956
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Actionable 1 0.61 0.58 0.59 760
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Benign 2 0.69 0.07 0.12 164
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Cancer 3 0.47 0.08 0.13 117
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accuracy 0.62 1997
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macro avg 0.60 0.38 0.39 1997
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weighted avg 0.61 0.62 0.58 1997
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```
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### FAQ
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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.
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[^1] Duke Study https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2783046
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[^2] [cancerimagingarchive https://www.breastcancer.org/facts-statistics](https://www.cancerimagingarchive.net/collection/breast-cancer-screening-dbt)
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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.
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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.
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Although in reality no easy feat, a disappointing statistic from the US's 7th best University.
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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.
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The result of this was creating a model which was evaluated at 62% using scikit-learn's classification report.
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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
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https://lnkd.in/eTx_sw9k |
