As a result of a lack of reliable tooling, much of the cell detection in microscopic imaging is still done manually. This in turn raises research and treatment costs. To tackle this problem, we developed a tool, which automatically detects and classifies normal and cancerous urothelial cells. In the first part the tool segments microscopic images and marks the discovered cell regions. On the basis of the discovered regions, the tool extracts a set of features, which are later used for learning classification models. Neural nets, random forests, naive Bayes classificator, decision rules, SVM, boosting and bagging were used for classification. We used both automatically and manually marked images of normal pig cells and cancerous human cells. Empirical observation shows, that the tool segments cells really well, nonetheless, we noticed that classificators perform better on manually marked cells.The best results were achieved (using manually marked cells) by neural nets (AUC (area under the curve) 0,9052), bagging (AUC 0,9041) and random forests (AUC 0,9005). The performance of the tool was further tested with cytopathological urine samples. The results of image segmentation with these samples were noticeably worse than with other image sets. With future enhancements this tool could considerably contribute to simpler and more reliable microscopic image analysis of cancerous cells.
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