This study focuses on the classification of chromosomes into 23 types, a step toward fully automatic karyotyping. This study proposes a convolution neural network (CNN) based deep learning network to automatically classify chromosomes. The proposed method was trained and tested on a dataset containing 10304 chromosome images and was further tested on a dataset containing 4830 chromosomes. The proposed method achieved an accuracy of 92.5%, outperforming three other methods that appeared in the literature. To investigate how applicable the proposed method is to the doctors, a metric named proportion of well-classified karyotype was also designed. A result of 91.3% was achieved on this metric, indicating that the proposed classification method could be used to aid doctors in genetic disorder diagnosis.
- SVM classifier
- K means clustering
- High Computational load
- Poor discriminatory power
- Less accuracy in classification
- Feature extraction
- Identification is attained accurately
- The pattern is classified by the neural network
- 4 GB of RAM
- 500 GB of Hard disk
- Anaconda navigators
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