Breast Cancer Prediction for Improved Diagnosis

Breast cancer is one of the most common cancer today in women. A breast mass in patients means a tumor. It does not necessarily imply a malignant one. Hence, American oncologists perform a fine needle aspirate (FNA) on the cancer patient.

Then, they examine the resulting cells and extract the cells nuclei features. The most critical step is this feature extraction.

The goal is to select elements of this image that one can measure for further computational analysis. And at the same time, the measures should be representative of cancer severity. We aim to use elements of the image measured as either a diagnostic or a prognostic indicator. It includes tumor malignancy and a related survival rate.

The typical features extracted are:

1. radius
2. texture
3. perimeter
4. area
5. smoothness
6. compactness
7. concavity (severity of concave portions of the contour)
8. concave points (number of concave portions of the contour)
9. symmetry
10. fractal dimension.

The diagnosis of cancer has been mostly dependent on the traditional approaches, using trained professionals’ expertise. However, a senior trained professional is not always available.

TADA’s Machine Learning approach can help automate, in part, the cancer risk prediction. Thus senior and junior professionals alike get access to the same analyzed data from cancer patients. They can provide a better, quicker diagnosis, hence improving survival rates.