Chromosome analysis method based on deep learning: Counting chromosomes and detecting abnormal chromosomes

•A novel deep learning based chromosome analysis method is proposed.•The proposed model called Unstable Chromosome Detector (UC-Det) conducts chromosome counting and abnormal detection based on object detection task.•Squeeze and excitation module and convolutional spatial pooling attention block is used for the proposed model.•With over 99% accuracy in counting and 75% accuracy in abnormal detecting, the proposed method outperforms the baseline method. Karyotype analysis is a cytogenetic test method that counts chromosomes and evaluates their structural abnormalities. However, since the processes of counting chromosomes and determining structural abnormalities are mostly performed manually, the analysis is time-consuming and labor-intensive, and the results may vary depending on the expert who performs the analysis. Therefore, studies for automating karyotype analysis have been conducted; however, most have focused on the detection of dicentric chromosomes among abnormal chromosome classes and counting the number of chromosomes. Therefore, this study proposes an automated chromosome analysis system that applies an object detection method based on deep learning to simplify chromosome analysis and derive effective results. The proposed analysis system consists of a chromosome counting system and an abnormal chromosome detection system. Additionally, to further improve the performance of the automated chromosome analysis system, convolutional spatial pooling attention and squeeze and excitation block are applied. In each proposed system, the object detection-based deep learning model detects a chromosome with a probability of 99.32%, and a chromosome abnormality with a probability of approximately 75.71%.