Blood Cancer Detection with Microscopic Images Using Machine Learning

K-means transformation, histogram equalization, linear contrast stretching, and share-based features are all used to detect leukemia. A method for automatically classifying leukocytes using microscopic images is proposed. This proposed model used MATLAB to find leukemia cells in healthy blood cells, and it requires no medical equipment or expert and heavily relies on automation. This technology can detect anemia, malaria, vitamin B12 deficiency, and brain tumors. The proposed method correctly identifies WBCs and leukoblasts in images and refines theidentification, thresholding, and segmentation phases. This improves WBC counting and overall segmentation accuracy, which leads to better shape feature extraction, which is critical for this problem. New features for this type of analysis must also be studied and analyzed. Finding the most discriminatory features will provide the best accuracy. Determining whether adjacent leukocytes can be separated is critical for counting all leukocytes in an image.