Chromosome analysis using a hybrid deep CNN and structural feature-based grouping model

Chromosome analysis and classification are essential in clinical applications to diagnose various structural and numerical abnormalities. Recently, karyotype analysis using intelligent image processing methods, especially deep learning, has attracted significant attention as a genetic abnormality test. This paper presents a novel chromosome classification algorithm that uses high-level features extracted from deep convolutional neural networks (DCNN) along with morphological features designed to identify and modify the classes of misclassified chromosomes. Initially, chromosomes are classified using a DCNN. Some structural features, such as centromere and banding profile, are then extracted to group chromosomes again. Based on the results of the two preceding methods, a decision strategy is utilized to identify misclassified chromosomes. Here, a final DCNN-based strategy is introduced to assign misclassified chromosomes to the associated classes. The proposed method can be used in parallel with other chromosome classification methods to modify misclassified chromosomes and promote the accuracy of the classification. Evaluation results show that the proposed algorithm outperforms relevant state-of-the-art algorithms regarding the classification precision and accuracy of 99.66 and 96.52%, respectively.