ALL-SESSA Deep Feature Optimization
This webpage contains links to all code and data for the paper
"Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features" by Ahmed T. Sahlol, Philip Kollmannsberger and Ahmed A. Ewees, published in Scientifc Reports 2020 and freely available online at www.nature.com/articles/s41598-020-59215-9
Code
The entire pipeline consists of three phases:
1) Applying very deep convolutional neural networks for feature extraction
This part was done using VGG19 pretrained on ImageNet in Keras. The code is in the following Jupyter Notebook:
Feature_Extraction_VGG19.ipynb
2) Feature selection using the Salp Swarm Algorithm (SSA)
The MATLAB implementation of SSA can be downloaded here.
3) Statistical enhancements to SSA for improved classification
These operations were implemented using scikit-learn, specifically:
- SelectKBest for univariate selection
- Recursive Feature Elimination (RFE), and
- Feature Importance using the ExtraTreesClassifier
The code is in the following Jupyter Notebook:
Statistical_Operations_Classification.ipynb
Data
The datasets used in this study can be downloaded here:
