Automatic detection of a structural chromosomal abnormality: the del(5q) deletion

Abstract

Cancer, hematological malignancies and inherited genetic diseases can be diagnosed by detecting chromosome abnormalities. This detection is crucial for the management and follow-up of these diseases. Biologically, there are two categories of chromosome abnormalities: either in their number or in their structure. The process of karyotyping involves creating an ordered representation of the 23 pairs of chromosomes. Each given pair presents a specific band pattern, where both chromosomes are identical, in normal cases. Karyotype images are manually analyzed by qualified cytogeneticists to detect any changes on chromosomes. Based on computer vision methods, it is possible to automate the detection of chromosome abnormalities, which can assist cytogeneticists in the diagnosis process. In the literature, little research has been done to automate the detection of structural abnormalities based on computer vision techniques. In this study, we are interested in the detection of a specific abnormality: the deletion of the long arm of chromosome 5, named del(5q) deletion. We focused our work on the use of the convolutional neural network (CNN) approach, which has shown its ability to provide reliable solutions in computer vision problems. On a collected database, we trained three CNN models to test their ability to differentiate between a healthy and an deleted chromosome 5. The highest performance was provided by VGG19, achieving an accuracy of 98.66%, a sensitivity of 89.33% and specificity of 100%.