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• W2912101381 abstract "Karyotyping plays a crucial role in genetic disorder diagnosis. Currently Karyotyping requires considerable manual efforts, domain expertise and experience, and is very time consuming. Automating the karyotyping process has been an important and popular task. This study focuses on classification of chromosomes into 23 types, a step towards fully automatic karyotyping. This study proposes a convolutional 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 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. An 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." @default.
• W2912101381 created "2019-02-21" @default.
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• W2912101381 date "2018-10-01" @default.
• W2912101381 modified "2023-09-29" @default.
• W2912101381 title "Chromosome Classification with Convolutional Neural Network Based Deep Learning" @default.
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• W2912101381 doi "https://doi.org/10.1109/cisp-bmei.2018.8633228" @default.
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