Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft‐tissue tumors in T1‐MRI images

Abstract

Purpose: To study, from a machine learning perspective, the performance of several machine learning classifiers that use texture analysis features extracted from soft-tissue tumors in nonenhanced T1-MRI images to discriminate between malignant and benign tumors. Results: The SVM classifier performs better than the neural network and the C4.5 decision tree based on the analysis of their receiver operating curves (ROC) and cost curves. The classification accuracy of the SVM, which was 93% (91% specificity; 94% sensitivity), was better than the radiologist classification accuracy of 90% (92% specificity; 81% sensitivity). Conclusion:Machine learning classifiers trained with texture analysis features are potentially valuable for detecting malignant tumors in T1-MRI images. Analysis of the learning curves of the classifiers showed that a training data size smaller than 100 T1-MRI images is sufficient to train a machine learning classifier that performs as well as expert radiologists.

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

“…In clinical diagnosis, the signs of abnormality observed by expert radiologists are very diverse. They include the size, contrast, intensity and density [1,[3][4][5][6][7][8][9][10][11][12][13][14][15].…”

“…Significant research on breast DCE-MRI lesion classification methods have already been made to automatically predict lesions such as artificial neural networks, linear discriminant analysis, logistic regression and support vector machines [1,3]. The artificial neural networks based classifier have been one of the most popular approaches for investigating the classification of malignant and benign breast DCE-MR lesions [1,[3][4][5][6][7][8][9][10][11][12][13][14][15]. The performance of any classifier depends on type of features used, training dataset provided and the type of classifier.…”

“…Texture feature has been proven to be useful in differentiating normal and abnormal pattern. For breast DCE-MRI suspicious lesion classification statistical texture features are widely used [3,4,[8][9][10][11][12][13][17][18][19][20].…”

Classification of Soft Tissue Tumors by Machine Learning Algorithms

“…The classifier should be comprehensively tested based on a prospective study before using the classifier. A shorter preliminary version of this chapter was published in Juntu et al (2010).…”

Cost-Sensitive Classifier Evaluation Using Cost Curves

“…Cost curves were used for this comparison, in preference to ROC curves, because "they are easier to interpret in meaningful units and they facilitate the selection of the best classifier by simple visualization" (p. 8). Juntu et al (2010) use ROC curves and cost curves to evaluate three machine learning methods to distinguish between benign and malignant soft-tissue tumours. They remark that cost curves are "much better for comparison between classifiers, especially when the ROC curves cross" (p. 685).…”