Project4-ImageSegmentationNucleiCell

To get the mask of the image segmentation of cell nuclei link: https://www.kaggle.com/c/data-science-bowl-2018

Summary

• 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

IDE and Framework

• The project built with Spyder as the main IDE
• use Tensorflow, Keras, Numpy, Mathplot

Methodology

• The folder contain 2; train folder ( 603 images) and test (63 images) folder;

• each folder contain the image folder and mask folder for all teh image of nuclei cell

• 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

Model

• 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:

Model Prediction

Predicting the first 5 images in test