Version 1
: Received: 29 April 2024 / Approved: 29 April 2024 / Online: 29 April 2024 (15:32:15 CEST)
How to cite:
Bose, P.; Bandyopadhyay, S. K. A Comprehensive Assessment And Classification Of Acute Lymphocytic Leukemia. Preprints2024, 2024041930. https://doi.org/10.20944/preprints202404.1930.v1
Bose, P.; Bandyopadhyay, S. K. A Comprehensive Assessment And Classification Of Acute Lymphocytic Leukemia. Preprints 2024, 2024041930. https://doi.org/10.20944/preprints202404.1930.v1
Bose, P.; Bandyopadhyay, S. K. A Comprehensive Assessment And Classification Of Acute Lymphocytic Leukemia. Preprints2024, 2024041930. https://doi.org/10.20944/preprints202404.1930.v1
APA Style
Bose, P., & Bandyopadhyay, S. K. (2024). A Comprehensive Assessment And Classification Of Acute Lymphocytic Leukemia. Preprints. https://doi.org/10.20944/preprints202404.1930.v1
Chicago/Turabian Style
Bose, P. and Samir Kumar Bandyopadhyay. 2024 "A Comprehensive Assessment And Classification Of Acute Lymphocytic Leukemia" Preprints. https://doi.org/10.20944/preprints202404.1930.v1
Abstract
Leukemia is a form of blood cancer that results in an increase in the number of white blood cells in the body. The correct identification of Leukemia at any stage is essential. The current traditional approaches rely mainly on field expertise' knowledge, which is time consuming. The poor understanding and a long period of examination might damage the human body. In this situation an automated Leukemia identification delivers more reliable and accurate diagnostic information.
To effectively diagnose acute lymphoblastic Leukemia from blood smears pictures, a new strategy based on traditional image analysis techniques with machine learning techniques, and a composite learning approach was constructed in this experiment. To identify the type of acute Leukemia first, four well-known machine learning models were utilized. It was discovered that Support Vector Machine (SVM) Provides the highest accuracy in this scenario. To boost the performance, a deep learning model Resnet50 was hybridized with SVM model. Finally, it was revealed that this composite approach achieved 99.9% accuracy.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
