Automated classification of metaphase chromosomes: Optimization of an adaptive computerized scheme

  • Authors:

  • Xingwei Wang;Bin Zheng;Shibo Li;John J. Mulvihill;Marc C. Wood;Hong Liu

  • Affiliations:

  • Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, 202 West Boyd Street, Room 219, Norman, OK 73019, USA;Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA;Department of Pediatrics, University of Oklahoma Health Science Center, Oklahoma City, OK 73104, USA;Department of Pediatrics, University of Oklahoma Health Science Center, Oklahoma City, OK 73104, USA;Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, 202 West Boyd Street, Room 219, Norman, OK 73019, USA;Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, 202 West Boyd Street, Room 219, Norman, OK 73019, USA

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2009

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Abstract

We developed and tested a new automated chromosome karyotyping scheme using a two-layer classification platform. Our hypothesis is that by selecting most effective feature sets and adaptively optimizing classifiers for the different groups of chromosomes with similar image characteristics, we can reduce the complexity of automated karyotyping scheme and improve its performance and robustness. For this purpose, we assembled an image database involving 6900 chromosomes and implemented a genetic algorithm to optimize the topology of multi-feature based artificial neural networks (ANN). In the first layer of the scheme, a single ANN was employed to classify 24 chromosomes into seven classes. In the second layer, seven ANNs were adaptively optimized for seven classes to identify individual chromosomes. The scheme was optimized and evaluated using a ''training-testing-validation'' method. In the first layer, the classification accuracy for the validation dataset was 92.9%. In the second layer, classification accuracy of seven ANNs ranged from 67.5% to 97.5%, in which six ANNs achieved accuracy above 93.7% and only one had lessened performance. The maximum difference of classification accuracy between the testing and validation datasets is