Model Ensembeling: A simple way of improving model performance for chromosome classification

Abstract

This paper deals with chromosome classification via convolutional neural networks and model ensembling. Chromosome classification is a part of a procedure in karyotyping, where the chromosomes should be paired and ordered so that they are prepared for inspection of abnormalities. Model ensembling was used as a technique to improve overall classification accuracy by using all of the trained models. We achieved 94.8 \% accuracy for a Q-band BioImlab dataset and 97.48 \% for a G-band chromosome CIR dataset.