This project solves the problem of classifying white blood cells into four types using handcrafted image features and a Gaussian Naive Bayes classifier. It works by segmenting the white blood cells from the background, extracting features such as color histograms and texture, and then classifying them using the Gaussian Naive Bayes algorithm. This project is useful for medical professionals and researchers who need to accurately classify white blood cells. It stands out from similar projects due to its high accuracy and robust feature extraction methods.
View on GitHub →abdullahh-sheikhh/white-blood-cell-classification-using-naive-bayes