Abstract
A chromosome is a DNA molecule that contains the genetic material of an organism. Possible defects in chromosomes can cause structural and functional disorders in living things. Identifying the metaphase stages of cells is a critical step to identify problems in chromosomes. In this proposed study, the discriminative features of possible metaphase images were extracted with Gray Level Co-occurrence Matrix and classified with the Extreme Learning Machines classification method. When the results obtained were evaluated, it was observed that the proposed method was as successful as the deep learning methods in the literature. Especially in recent years, when online learning has become important, the need for re-training of deep learning-based algorithms after each validation will increase the importance of the proposed method in this field. The rapid increase in unlabeled data from each patient every day affects the duration of training and creates time and resource constraints. Fast and accurate modeling of such data with alternative machine learning methods will contribute to the studies in this area.
Highlights
Cytogenetics is a branch of genetics which concerned with the study of number, structure and function of chromosomes
After deep learning-based metaphase detection methods such as GoogleNet, VGG16, Inception, LeNet, which were quite successful in our previous study in the literature and we suggested before, experts were provided to present metaphase images with higher success
In this proposed study, the feature vectors of the possible metaphase images were extracted with the gray level co-occurrence matrix (GLCM) algorithm and classified with the Extreme Learning Machines (ELM) algorithm
Summary
Cytogenetics is a branch of genetics which concerned with the study of number, structure and function of chromosomes. Experts try to detect the anomaly by analyzing genes obtained by sampling special tissues such as bone marrow, blood, amniotic fluid or placenta in order to detect possible congenital syndromes. This process performed in the expert's cytogenetics laboratory is called karyotyping. There are various studies proposed in the literature to handle this purpose These approaches can generally be divided into two groups as rulebased and deep learning-based image processing methods [1]. After deep learning-based metaphase detection methods such as GoogleNet, VGG16, Inception, LeNet, which were quite successful in our previous study in the literature and we suggested before, experts were provided to present metaphase images with higher success
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