/Malaria-Detection

Malaria Detection Project on Malaria Cells

Primary LanguageJupyter Notebook

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