A Hierarchical Artificial Neural Network Model for Giemsa-Stained Human Chromosome Classification

This paper proposes an improved two-step classification scheme for Giemsa-stained human chromosomes using a hierarchical multilayer neural network with an error back-propagation training algorithm. In the first step, the Group Classifier (GC), a two-layer neural network, classifies chromosomes into seven groups based on their morphological features, such as relative length, relative area, and the centromeric index and 80 density values. In the second step, seven Subgroup Classifiers (SCs), which are also two-layer neural networks, classify the chromosomes in each group into 24 subgroups based on the same features used in the first step. The optimal parameters for the GC and SCs, including the number of processing elements in the hidden layer, were determined experimentally. The optimized GC and SCs were trained using a training dataset and tested using the same test dataset used in a previous study [3]. The overall classification error rate decreased to 5.9% using the two-step classification scheme, which is better than the result in the previous study, which used a single-step multilayer neural network as a classifier and achieved a 6.52% classification error rate [3]. This paper shows that the classification accuracy for Giemsa-stained human chromosomes can be improved using a two-step classification scheme rather than a single-step classification.