C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Information Retrieval
Inference for the Generalization Error
Machine Learning
Comparison of Five Color Models in Skin Pixel Classification
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Automatic Color Space Selection for Biological Image Segmentation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Head and Neck Cancer Detection in Histopathological Slides
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Journal of Signal Processing Systems
Delineation of malignant areas in histological images of head-neck cancer
Delineation of malignant areas in histological images of head-neck cancer
Effect of colorspace transformation, the illuminance component, and color modeling on skin detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Color is the most critical information for assessing histological images. However, in literature, there is no standard color space in which a particular color points are represented for computer vision tasks. In this paper, we evaluated 11 color models with three different learning schemas for their performance in classifying tumor-related colors. The color models we studied are CIELAB, CIELUV, CIEXYZ, CMY, CMYK, HSL, HSV, Hunter-LAB, NRGB, RGB, and SCT. With 11 color models, prediction accuracies of three well-known classifiers, namely SVMs, C4.5, and Naïve Bayes, are statistically compared on a large dataset of 3494 Hematoxylin and Eosin (HE) stained histopathologic images. Surprisingly, experiment results show that in contrast to general assumptions, there is no single model that is better than others in every case. However, C4.5 outperformed other two classifiers by obtaining average F-measure of 0.9989. Of 11 color models, we suggest the pair of C4.5-SCT as the most accurate classification framework for tumor identification in HE stained histological images.