Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images.
In this paper, we investigate the performance of classification of protein crystallization images captured during protein crystal growth process. We group protein crystallization images into 3 categories: noncrystals, likely leads (conditions that may yield formation of crystals) and crystals. In this research, we only consider the subcategories of noncrystal and likely leads protein crystallization images separately. We use 5 different classifiers to solve this problem and we applied some data preprocessing methods such as principal component analysis (PCA), min-max (MM) normalization and z-score (ZS) normalization methods to our datasets in order to evaluate their effects on classifiers for the noncrystal and likely leads datasets. We performed our experiments on 1606 noncrystal and 245 likely leads images independently. We had satisfactory results for both datasets. We reached 96.8% accuracy for noncrystal dataset and 94.8% accuracy for likely leads dataset. Our target is to investigate the best classifiers with optimal preprocessing techniques on both noncrystal and likely leads datasets.
DataMedia Research Lab, Computer Science Department, University of Alabama in Huntsville, Huntsville Alabama 35899.
İmren
İ
Dinç
DataMedia Research Lab, Computer Science Department, University of Alabama in Huntsville, Huntsville Alabama 35899.
Madhav
M
Sigdel
DataMedia Research Lab, Computer Science Department, University of Alabama in Huntsville, Huntsville Alabama 35899.
Semih
S
Dinç
DataMedia Research Lab, Computer Science Department, University of Alabama in Huntsville, Huntsville Alabama 35899.
Madhu S
MS
Sigdel
iXpressGenes Inc., 601 Genome Way, Huntsville, Alabama 35806.
Marc L
ML
Pusey
DataMedia Research Lab, Computer Science Department, University of Alabama in Huntsville, Huntsville Alabama 35899.
Ramazan S
RS
Aygün
classification; normalization; principal component analysis; protein crystallization