Artificial neural networks : A comparative study of implementations for human chromosome classification

Artificial neural networks are a popular field of artificial intelligence and have commonly been applied to solve many prediction, classification and diagnostic tasks. One such task is the analysis of human chromosomes. This thesis investigates the use of artificial neural networks (ANNs) as automated chromosome classifiers. The investigation involves the thorough analysis of seven different implementation techniques. These include three techniques using artificial neural networks, two techniques using ANN s supported by another method and two techniques not using ANNs. These seven implementations are evaluated according to the classification accuracy achieved and according to their support of important system measures, such as robustness and validity. The results collected show that ANNs perform relatively well in terms of classification accuracy, though other implementations achieved higher results. However, ANNs provide excellent support of essential system measures. This leads to a well-rounded implementation, consisting of a good balance between accuracy and system features, and thus an effective technique for automated human chromosome classification