This use case cover a breast cancer binary classification using microscopic biopsy images Worldwide, breast cancer is the most-common invasive cancer in women. Along with lung cancer, breast cancer is the most commonly diagnosed cancer, with 2.09 million cases each in 2018. Breast cancer affects 1 in 7 (14%) of women worldwide. This types of algorithms can help to increase the number of diagnosis and discard the true negatives, leaving only the positives and false negatives to doctors.
Datasets
The original dataset is divided on train and test forlders with two classes, malignant and benign, but Perceptilabs has the capabilities to divide the dataset in train, test and validation, then we merge the full dataset in a single dataset with the following distribution:
You can access to the new dataset using perceptilabs github.
Model
| Layer | Configuration |
|---|---|
| ResNet50 | include_top=false, pretrained=imagenet |
| Dense | Activation=ReLU, Neurons=128 |
| Dense | Activation=ReLU, Neurons=64 |
| Dense | Activation=SoftMax, Neurons=2C |
Workspace
Statistics view
Accuracy plot