On the design of a fuzzy relational neural network for automatic speech recognition
On the design of a fuzzy relational neural network for automatic speech recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery
Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
| Hi-index | 0.00 |
Leukemia is a disease characterized by an abnormal increase of white blood cells. This disease is divided into two types: lymphoblastic and myeloid, each of which is divided in subtypes. Differentiating the type and subtype of acute leukemia is important in order to determine the correct type of treatment to be assigned by the affected person. Diagnostic tests available today, such as those based on cell morphology, have a high error rate. Others, as those based on cytometry or microarray, are expensive. In order to avoid those drawbacks this paper proposes the automatic selection of a fuzzy model for accurate classification of types and subtypes of acute leukemia based on cell morphology. Our experimental results reach up to 93.52% in classification of acute leukemia types, 87.36% in lymphoblastic subtypes and 94.42% in myeloid subtypes. Our results show a significant improvement compared with classifiers which parameters were manually tuned using the same data set. Details of the proposed method, as well as experiments and results are shown.