Research Article
Detection of Leukemia Using K-Means Clustering and Machine Learning
@INPROCEEDINGS{10.1007/978-3-030-79276-3_15, author={V. Lakshmi Thanmayi A and Sunku Dharahas Reddy and Sreeja Kochuvila}, title={Detection of Leukemia Using K-Means Clustering and Machine Learning}, proceedings={Ubiquitous Communications and Network Computing. 4th EAI International Conference, UBICNET 2021, Virtual Event, March 2021, Proceedings}, proceedings_a={UBICNET}, year={2021}, month={7}, keywords={Leukemia White Blood Cells Machine learning Support Vector Machine}, doi={10.1007/978-3-030-79276-3_15} }- V. Lakshmi Thanmayi A
Sunku Dharahas Reddy
Sreeja Kochuvila
Year: 2021
Detection of Leukemia Using K-Means Clustering and Machine Learning
UBICNET
Springer
DOI: 10.1007/978-3-030-79276-3_15
Abstract
Leukemia or blood cancer is a common and serious disease across countries which is caused due to the sudden increase in White Blood Cells (WBCs) in blood. This increase in WBC is due to the production of immature or blast cells in the bone marrow of the affected person. Detection and diagnosis at early stage is important. Additionally, computer-aided diagnosis will enhance the process of detection with better accuracy. In this paper, we developed an algorithm for early-stage detection of leukemia using image processing. We also used machine learning classification techniques to classify between cancerous and non-cancerous cells. The algorithm uses K-means clustering for the segmentation of images and a linear Support Vector Machine (SVM) classifier for the classification. ALL-IDB data set has been used to validate the algorithm. A total of 368 images are used in the algorithm. Algorithm offers 95% of accuracy and an approximate 93% of precision.
