Towards an autonomous human chromosome classification system using Competitive Support Vector Machines Teams (CSVMT)

In broad terms, karyotyping is the process of examination and classification of human chromosome images to diagnose genetic diseases and disorders. It requires time consuming manual examination of cell images by a cytogeneticist to distinguish chromosome classes from each other. Thus, a reliable autonomous human chromosome classification system not only saves time and money but also reduces errors due to the inadequate knowledge level of the expert. Human cell contains 23 pairs of chromosome, 22 autosomes and a pair of sex chromosomes. Hence, we face a multi-class classification task which represents a challenging case for any sort of classifier. In this work, to solve this classification problem, we propose a novel methodology consisting two stages: (i) data preparation and training, and (ii) testing. To determine the most informative content of the dataset several preliminary experiments are conducted and a Principal Component Analysis is done. Then, a single Support Vector Machine (SVMij) is trained to separate a pair of classes, (i,j) where a numerical optimization method Pattern Search (PS), is employed to find the optimal parameters for the SVMij. Considering 22 pairs of autosomes, 22 × 22 experts are trained and optimized. The cluster of experts, we obtain is named as Competitive SVM Teams (CSVMTs) where each SVMij competes with the others to label a new classification instance. The final output of the classifier is determined by majority voteing. The results obtained on Copenhagen dataset proves the merit of the algorithm as correct classification rates (CRR) on train and test samples are 99.55% and 97.84% respectively, which are higher than any accuracy rate achieved so far in the related literature.