Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Experimental Comparison of Feature Subset Selection Methods
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Texture based classification of hyperspectral colon biopsy samples using CLBP
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Computational Biology and Chemistry
Learning methods for melanoma recognition
International Journal of Imaging Systems and Technology
Neural system for heartbeats recognition using genetically integrated ensemble of classifiers
Computers in Biology and Medicine
Face detection with effective feature extraction
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Detection of masses in mammogram images using CNN, geostatistic functions and SVM
Computers in Biology and Medicine
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE Transactions on Information Technology in Biomedicine
Fractal analysis in the detection of colonic cancer images
IEEE Transactions on Information Technology in Biomedicine
Combining classifiers under probabilistic models: experimental comparative analysis of methods
Expert Systems: The Journal of Knowledge Engineering
Color texture segmentation based on image pixel classification
Engineering Applications of Artificial Intelligence
Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering
Computer Methods and Programs in Biomedicine
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders
Computers in Biology and Medicine
An ensemble of SVM classifiers based on gene pairs
Computers in Biology and Medicine
A Recent Survey on Colon Cancer Detection Techniques
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 0.00 |
In recent years, classification of colon biopsy images has become an active research area. Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process is subjective and leads to considerable inter/intra observer variation. Therefore, reliable computer-aided colon cancer detection techniques are in high demand. In this paper, we propose a colon biopsy image classification system, called CBIC, which benefits from discriminatory capabilities of information rich hybrid feature spaces, and performance enhancement based on ensemble classification methodology. Normal and malignant colon biopsy images differ with each other in terms of the color distribution of different biological constituents. The colors of different constituents are sharp in normal images, whereas the colors diffuse with each other in malignant images. In order to exploit this variation, two feature types, namely color components based statistical moments (CCSM) and Haralick features have been proposed, which are color components based variants of their traditional counterparts. Moreover, in normal colon biopsy images, epithelial cells possess sharp and well-defined edges. Histogram of oriented gradients (HOG) based features have been employed to exploit this information. Different combinations of hybrid features have been constructed from HOG, CCSM, and Haralick features. The minimum Redundancy Maximum Relevance (mRMR) feature selection method has been employed to select meaningful features from individual and hybrid feature sets. Finally, an ensemble classifier based on majority voting has been proposed, which classifies colon biopsy images using the selected features. Linear, RBF, and sigmoid SVM have been employed as base classifiers. The proposed system has been tested on 174 colon biopsy images, and improved performance (=98.85%) has been observed compared to previously reported studies. Additionally, the use of mRMR method has been justified by comparing the performance of CBIC on original and reduced feature sets.