A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
| Hi-index | 0.00 |
In this paper application of kernel based learning algorithms to endoscopy images classification problem is presented. This work is a part the attempts to extend the existing recommendation system (ERS) with image classification facility. The use of a computer-based system could support the doctor when making a diagnosis and help to avoid human subjectivity. We give a brief description of the SVM and LS-SVM algorithms. The algorithms are then used in the problem of recognition of malignant versus benign tumour in gullet. The classification was performed on features based on edge structure and colour. A detailed experimental comparison of classification performance for diferent kernel functions and different combinations of feature vectors was made. The algorithms performed very well in the experiments achieving high percentage of correct predictions.