---
license: mit
language:
  - en
tags:
  - image classification
  - classification
  - medical imaging
  - medical
  - dicom
  - cancer
metrics:
  - '62% Sensitivity'
---

# HerBreastsFriend(HBF)

![Demo](./assets/imgs-preview.gif)

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

![Demo](./assets/matrix-previews.gif)

- 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)