Classification of Metaphase Chromosomes Using Deep Convolutional Neural Network.

Abstract

Karyotype analysis has important clinical significance in the diagnosis, treatment, and prognosis of diseases such as birth defects and hematological tumors. Identifying chromosomes and their structure variations from G-banded metaphase images is an important process in karyotyping, and also is the most difficult one. Automatic chromosome classification becomes urgent in recent years since more and more samples of patients are subject to medical test such as bone marrow biopsy. With the development of artificial intelligence, convolutional neural networks (CNNs) have shown good performance in image recognition. In this study, a CNN with 6 convolutional layers, 3 pooling layers, 4 dropout layers, and 2 fully connected layers was trained using the labeled data set to classify chromosomes into 24 classes through softmax activation function mapping. The classifier gave an accuracy of 93.79% for chromosome identification. The result demonstrated that the CNN has potential application value in chromosome classification and will contribute to the construction of an automatic karyotyping platform.