Breast_cancer_detection

Using pre-trained model to classify images to detect cancerous cells

Pre-requirements:

1. Python2.7
2. MATLAB (LIBSVM)
3. Numpy, Scipy,Sklearn
4. Tensorflow 1.0
5. Tflearn

BreakHis dataset can be found at: http://web.inf.ufpr.br/vri/breast-cancer-database The VGG-16 weights are being used from Davi Frossard's webpage here: https://www.cs.toronto.edu/~frossard/vgg16/vgg16_weights.npz Here is a clean introductory tutorial by him: https://www.cs.toronto.edu/~frossard/post/vgg16/

We try pre-trained network and classification vs training from scratch. Method1: Using pre-trained VGG-16 to get features.

1. Run vgg16_cv.py to extract the features from each image of BreakHis dataset. It will create one feature file per image int he same folder
2. Run generate_features.py to combine all individual feature files into one feature matrix (mat file). It also creates a separate target mat file.
3. Run CV_balancing_code.m to treat the data imbalance. It outputs 4 files: training data, training data targets, test data and test data targets
4. Use classifier_code.m and RandomForest_CV.m to classify the data using Linear SVM, Polynomial SVM and Random Forest.

Method 2: Run alexnet.py to get the trained AlexNet model and confusion matrix.

Analysis can be found at https://medium.com/womeninai/classifying-biological-images-using-pre-trained-cnns-102060de0687