Classification of leukemia blood samples using neural networks.
Pattern recognition applied to blood samples for diagnosing leukemia remains an extremely difficult task which frequently leads to misclassification errors due in large part to the inherent problem of data overlap. A novel artificial neural network (ANN) algorithm is proposed for optimizing the classification of multidimensional data, focusing on acute leukemia samples. The programming tool established around the ANN architecture focuses on the classification of normal vs. abnormal blood samples, namely acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). There were 220 blood samples considered with 60 abnormal samples and 160 normal samples. The algorithm produced very high sensitivity results that improved up to 96.67% in ALL classification with increased data set size. With this type of accuracy, this programming tool provides information to medical doctors in the form of diagnostic references for the specific disease states that are considered for this study. The results obtained prove that a neural network classifier can perform remarkably well for this type of flow-cytometry data. Even more significant is the fact that experimental evaluations in the testing phase reveal that as the ALL data considered is gradually increased from small to large data sets, the more accurate are the classification results.
Adjouadi
Malek
M
Department of Electrical & Computer, Center for Advanced Technology and Education, Florida International University, 10555 W. Flagler Street, EAS 2672, Miami, FL 33174, USA. adjouadi@fiu.edu
Ayala
Melvin
M
Cabrerizo
Mercedes
M
Zong
Nuannuan
N
Lizarraga
Gabriel
G
Rossman
Mark
M
D000465:Algorithms; D001772:Blood Cell Count / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D007938:Leukemia / Q000097:blood* / Q000473:pathology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity