Convolutional Neural Network

Classification and detection of multiple myeloma with deep learning.

Quick start

This repository contains Matlab, Python, and R scripts for machine learning and deep learning classification experiments. See DEPENDENCIES.md for the required Matlab toolboxes, Python packages, and R packages.

Repository contents

• Training_DeTraC.m, Training_original_classes.m, createLgraphUsingConnections.m, findLayersToReplace.m, construct_dataset_B.m, extract_features.m, Pca_CXR.m, ConfusionMat_MultiClass.m, readAndPreprocessImage.m -- Matlab deep-learning classifier training and feature analysis.
• DeTraC_model.ipynb -- a Python/TensorFlow-Keras ResNet50 classification notebook.
• cnn.py, CNN_test.py, DeepLearning_Keras.py, LogisticRegressionExample.py, XGBoost_Model_test.py -- standalone Python model examples (PyTorch/deepchem, Keras/TensorFlow, scikit-learn, XGBoost).
• kerasMNISTconvnet.R, testMNISTconvnet.R, mnist_convet.R, regressionUmiseq.R, txttojpg.R -- R scripts for Keras-in-R examples and genomics data preparation and regression analysis.
• figures/ -- a figure of the CNN network architecture I prepared.
• License: see LICENSE -- research/educational use.

About

I tested several deep learning and machine learning classification approaches (Keras/TensorFlow, PyTorch/DeepChem, scikit-learn, XGBoost) for multiple myeloma drug-efficacy classification, including the DeTraC COVID-19 chest X-ray classification algorithm (ResNet18) developed by the Gaber lab; for detailed information, see: https://www.researchgate.net/publication/382850918_Analysis_of_Multiple_Myeloma_Drug_Efficacy

Method from the Gaber lab - Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Medhat Gaber "Classification of COVID-19 in Chest X-ray Images Using DeTraC Deep Convolutional Neural Network" (2021), https://pubmed.ncbi.nlm.nih.gov/34764548/; code: https://github.com/asmaa4may/DeTraC_COVId19/tree/master/python_code