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Glaucoma and Non-Glaucoma classification using ML/Dl and ensemble approaches using Image Feature Extraction Using HOG (Histogram of Gradient)

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officialarijit/Glaucoma-classification-ML-DL

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Glaucoma and Non-Glaucoma classification using ML/Dl and ensemble approaches using Image Feature Extraction Using HOG (Histogram of Gradient)

Packages

  • Jupyter-Notebook : will be used to run the programs.

    pip install scikit-learn

  • Open CV : For reading and manipulating images.

    pip install opencv-python

  • Numpy : used for multi-dimensional arrays and matrices.

    pip install numpy

  • Sklearn : will be used to get PCA for dimensionality reduction.

    pip install scikit-learn

  • Mlxtend : will be used to implement the Majority Voting Ensemble approach.

    pip isntall mlxtend

Run The Program

  • Run the program by executing the below code.

    run Feature Extraction HOG.ipynb

Result

  • Result is generated and placed inside the

    100_extracted_features.csv.

Classification

  • Once the features are extracted then run the following code. run Glaucoma-Ensemble-Approach.ipynb

    -- In this file Multi-Layer Perceptron (MLP), SVM and Random Forest (RF) classifiers are available. Also, the Majority Voting Ensemble approach is available.

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Glaucoma and Non-Glaucoma classification using ML/Dl and ensemble approaches using Image Feature Extraction Using HOG (Histogram of Gradient)

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