Classification of Metaphase Chromosomes Using Deep Learning Neural Network

Karyotyping of Banded Metaphase Chromosomes is one of the preliminary steps used in cytogenetics to analyze the chromosomes for diagnostic purposes. Deep learning is a subfield of machine learning concerned with structure and function of brain. It exploits a way to automate predictive analysis. The key aspect of deep learning is that the layers of features are not designed by human engineers. They are learned from data using a general purpose learning procedure. This paper proposes a convolution based deep learning to classify the chromosomes for automated karyotyping. The developed architecture allows us to train and test images that helps in predicting the chromosome abnormality. The performance analysis is based on loss and accuracy curves and the graphical representation clearly exhibits better classification results for this architecture.