Problem

The diagnosis of breast cancer is a complex and time-consuming process that requires the analysis of digitized images of breast biopsy slides. The traditional method of diagnosis is based on the visual interpretation of these images by a pathologist, which can lead to errors and inconsistencies in the results. This process can also be time-consuming, leading to delays in treatment for patients.

To address this problem, our team was tasked with developing a machine learning classifier that could accurately predict whether a breast tumor is malignant or benign based on cell nucleus features extracted from digitized images of breast biopsy slides.

Learn more about our project here.

Solution

Our team began by collecting and cleaning a dataset of digitized images of breast biopsy slides. We then used image processing techniques to extract relevant features from each image, such as the size and shape of cell nuclei. These features were used to train a machine learning classifier using various algorithms such as Decision Tree, Random Forest, KNN etc.

We evaluated the performance of our classifier using various metrics such as accuracy, precision, recall, F1-score etc., and fine-tuned it until we achieved optimal results. Our final model was able to accurately predict whether a tumor was malignant or benign with an accuracy rate above 95%.

Impact

Reduced diagnostic errors and inconsistencies in breast cancer diagnosis
Improved efficiency in diagnosing breast cancer by reducing the time required for analysis
X% increase in accuracy rate compared to traditional diagnosis methods

Our machine learning classifier has the potential to revolutionize breast cancer diagnosis and improve patient outcomes.

Team

Team Member #1 - Data Scientist
- Developed and trained the machine learning classifier using various algorithms such as Decision Tree, Random Forest, KNN etc.
- Evaluated the performance of the classifier using various metrics such as accuracy, precision, recall, F1-score etc.
Team Member #2 - Software Engineer
- Built a user-friendly interface for the classifier that allows pathologists to easily input digitized images of breast biopsy slides and receive accurate predictions about whether a tumor is malignant or benign.
- Fine-tuned the model until optimal results were achieved.

Breast Cancer Diagnosis with 95% Accuracy Using ML