.png){kind=icon}
Return
Ensemble learning using Gompertz function for leukemia classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bspc.2024.106925.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Leukemia, a life-threatening disease primarily affecting children below 15 and individuals aged 55 and above, is characterized by the overproduction of abnormal blood cells. Timely detection of leukemia plays a crucial role in significantly reducing the associated mortality rate. Given the recent developments in deep learning, the development of automated leukemia detection techniques using blood smear images has emerged as an important research area. This manuscript introduces a novel algorithm to classify microscopic blood smear images into five categories: AML, CML, ALL, CLL and normal. The study investigates the use of pre- trained convolutional neural networks powered by a fuzzy ensemble-based classifier. The fuzzy-based ensemble learning technique employs adaptable weights according to the confidence scores derived from the base learner models instead of using fixed weights for the base learner models. Gompertz function with parameters, alpha , beta and gamma are considered here. The parameters of the Gompertz function are further optimized using a grid search approach to achieve the most favorable classification outcomes. Conducting a 5-class classification, the proposed algorithm achieved an accuracy of 88.80%, surpassing the performance of traditional ensemble techniques such as average, weighted average and majority voting by a significant margin of 1.83%, 1.83% and 5.71%, respectively.
Keywords:
Leukemia
Convolutional neural networks
Fuzzy ensemble learning
Gompertz function
Majority voting
Journal
IF:
4.9
Papers:
9.9K
Citations:
2.4W
Organization
Cited Papers
ShareSaveShareSaveA Framework for Early Detection of Acute Lymphoblastic Leukemia and Its Subtypes From Peripheral Blood Smear Images Using Deep Ensemble Learning Technique.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSave
View more