Acute Leukemia Subtype Recognition in Blood Smear Images with Machine Learning
Acute Leukemia; cell analysis; Image processing; Leukemia Classification; Segmentation
Acute leukemia is a swiftly progressing blood cancer affecting white blood cells which poses a significant threat to the immune system and often leads to fatal outcomes if not detected and treated promptly. The current manual diagnostic method, being time-consuming and prone to errors, necessitates an urgent shift toward a comprehensive automated system. This paper presents an innovative approach to automatically identify acute leukemia cells and their subtypes by analyzing microscopic blood smear images. The proposed methodology involves the segmentation of clustered lymphocytes, isolation of nuclei, and extraction of diverse features from each nucleus. A random forest classifier is then trained to categorize nuclei into healthy or cancerous, with further precision in classifying cancerous nuclei into specific subtypes. The method achieves an impressive 97% accuracy across all evaluations, holding profound implications for pathologists and medical practitioners in their decision-making processes. 2024, J.J. Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved.
International Journal of Electrical and Computer Engineering Systems, Vol-15, No. 7, pp. 563-570.
J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology
https://doi.org/10.32985/ijeces.15.7.2 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199139463&doi=10.32985%2fijeces.15.7.2&partnerID=40&md5=9bd641d0c97f8c41adb4912167c1d886
Patil A.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Hiremath M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India