A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Machine Learning
Machine Learning
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
Applied Intelligence
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Multivariate image analysis in biomedicine
Journal of Biomedical Informatics
Cluster and Classification Techniques for the Biosciences
Cluster and Classification Techniques for the Biosciences
Preparation of 2D sequences of corneal images for 3D model building
Computer Methods and Programs in Biomedicine
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Anal intraepithelial neoplasia (AIN) is a precancerous condition of growing concern, due to the strong interrelation of AIN with infections caused by human papillomaviruses (HPV) and HIV. Several HPV-subtypes induce a variety of tumorous skin lesions and cause different stages of dysplasia and even cancer. The histological classification of AIN is becoming more and more important in clinical practice, due to increasing HPV infection rates throughout human population. Histological slices of anal tissues are commonly classified by individual inspections with all the unavoidable differences of the training status and variances of the individual. Therefore, a quantitative classification method including the calculations of first order as well as second order image statistical parameters in combination with data mining was developed. The results of several classifiers were compared to each other and it turned out that at least two classifiers had very high correct classification rates with very low errors. So it was possible to classify the distinct grades of AIN with high accuracy. The quantitative approach has the potential to minimize individual classification errors significantly and it will enable the establishing of a quantitative screening technique.