Reference no: EM132400015

CSCE822 Data mining Homework - Deep learning application for microscopy image classification

Install Keras deep learning framework. (If you can use Tensorflow or Pytorch, it is also ok)

Pip3 install tensorflow keras

Step 1: Study one of the following tutorials on how to apply basic convolutional neural network for image classification using the Keras deep learning framework or the simplest CNN code for digit number image classification.

Download sample code, and run the code, and report your training and test performance.

Step 2: Modify the sample code for digital number recognition and develop a deep learning algorithm based on convolutional neural networks for classifying the following 3 classes of Scanning Electron Microscopy images: fibres, sponge, and power. (See attached image files). Note that the image dimension of this image set is much larger (1024x768) than the mnist digit number recognition dataset.

You should use the first the 75% images as training, next 5% as validation set and remaining as test dataset. Try to compare the performance with or without max-pooling, batch-normalization, varying the number of convolution layers and convolution layers.

Attachment:- Data mining Homework File.rar