File size: 2,067 Bytes
67daf24 47d4b6c 67daf24 47d4b6c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
license: mit
language:
- python
thumbnail: "url to a thumbnail used in social sharing"
tags:
- DICOM
- cancer
- medical
- medical imaging
- classification
datasets:
- [dataset1](https://www.breastcancer.org/facts-statistics](https://www.cancerimagingarchive.net/collection/breast-cancer-screening-dbt)
metrics:
- 62% Sensitivity
base_model: "N/A"
---
# 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)
|