To get the mask of the image segmentation of cell nuclei link: https://www.kaggle.com/c/data-science-bowl-2018
- To make image segmentation of cell nuclei
- deal with a large dataset
- the deep learning model is used and trained
- The model use Transfer Learning from mobilenetV2
- The project built with Spyder as the main IDE
- use Tensorflow, Keras, Numpy, Mathplot
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The folder contain 2; train folder ( 603 images) and test (63 images) folder;
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each folder contain the image folder and mask folder for all teh image of nuclei cell
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Here some example of image and mask in the first 3 image and its mask in train folder
- We do the data normalization in the mask and the image in training data; the value is varies between 0 to 255.
- We want to change these value into ranging 0 to 1
- we did the train test split with 20% of the data is doing the test.
- we convert the numpy array of x_train, x_test, y_train, y_test into tensor slices.
- we Zip the tensor clices into ZipDataSet of train and test.
- As we deal a large amount of images and masks. we need to conver the train and test data into PrefetchDataSet. This is use to avoid bottle neck when import the images into dataset.
- we create the model; we use pretrained model from MobileNetV2 as base model and as feature extractor.
- make a few modification on the layer as we deal the image segmentation problem. Has a few activations layer as base model output. Has a downstack to define extraction model and freeze the trainable layer. Define the upsampling by using model pix2pix
- the model use modified Unet; use Functional API, Downsampling through the model then upsampling the model and establish teh skip connections between the downsampling and upsampling. The last layer we use Conv2DTranpose:
- We has 3 output class which is the pixel of nuclei cell, pixel bordering the nuclei cell and background pixel. ( in Image Segmentation, each pixel in the image need to assign to the its classes)
-The model is compile with optimizer of 'adam' with learning rate = 0.001, loss= SparseCrossentropy', metrics of accuracy, epochs of 20
-This is show how the image, mask and predicted mask before the model training:
EPOCHS=1
EPOCHS=20
The value is display by using TensorBoard:
Predicting the first 5 images in test
