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Malaria Detection

Project Overview

  • Compare Naive Bayes, SVM, XGBoost, Bagging, AdaBoost, K-NN, etc. for Malaria Cells classification
  • Used different feature extraction techniques like HOG, LBP, SIFT, SURF, pixel values
  • Feature reduction techniques PCA, LDA
  • Normalization techniques such as z-score and min-max
  • Classifiers such as Naive Bayes, SVM XGBoost, Bagging, AdaBoost, K-Nearest Neighbors, Random Forests
  • Metrics such as Accuracy, Precision, Recall, F1 score, and ROC

Dataset

Infected Cells

Uninfected Cells

Result