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This repository hosts the COMP683 final project: Tumor Type Prediction using Convolutional Neural Networks (CNNs). The classification results for 33 tumor types are summarized in the following table.

• src/: Contains all source files.
  • common_functions.py: Includes common functions for CNN training and testing.
  • image_transformer.py: Transforms 1-D features to 2-D images using feature similarity, adapted from DeepInsight.
  • process_tcga_dataset.py: Reads TCGA RNA-seq data for 33 tumor types and converts the sample-feature matrix into 2-D images.
  • test_cnns.py: Trains and evaluates prediction accuracy across 33 tumor types using various CNN architectures on converted 2-D images.
  • tcga_files.py: Lists predefined filenames and tumor names for the 33 tumor types, sourced from the UCSC Xena Portal.

• Visit the UCSC Xena Portal.
• Select the Harmonized dataset by clicking on the GDC hub checkbox.
• Download the required files listed in ./src/TCGA/tcga_file_list.txt into the ./src/TCGA folder

Navigate to the src directory, run the following Python script to convert the TCGA gene expression data into 2-D images:

python process_tcga_dataset.py

This script will generate a file named feat2img.bin, which includes all the 2-D images along with their corresponding labels.

Continue in the src directory, execute following Python script to train and evaluate the prediction accuracy for 33 tumor types using various CNN architectures, including SqueezeNet, MobileNet, ResNet18, and ResNet50:

python test_cnns.py