C4.5: programs for machine learning
C4.5: programs for machine learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A low variance error boosting algorithm
Applied Intelligence
Machine learning in medical imaging
Machine Vision and Applications
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Patient-targeted therapies have recently been highlighted as important. An important development in the treatment of metastatic non-small cell lung cancer (NSCLC) has been the tailoring of therapy on the basis of histology. A pathology diagnosis of "non-specified NSCLC" is no longer routinely acceptable; an effective approach for classification of adenocarcinoma (AC) and squamous carcinoma (SC) histotypes is needed for optimizing therapy. In this study, we present a robust and objective automatic computer vision system for real-time classification of AC and SC based on the morphological tissue patterns of hematoxylin and eosin (H&E) staining images to assist medical experts in the diagnosis of lung cancer. Various original and extended densitometric and Haralick's texture features are used to extract image features, and a boosting algorithm is utilized to train the classifier, together with alternative decision tree as the base learner. For evaluation, two types of data with 653 tissue samples were tested, including 369 samples from tissue microarray data set and 284 samples from full-face tissue sections. Regarding the data distribution, 45 % are AC samples (288) and 55 % are SC samples (365), which is considerably well balanced for each class. Using tenfold cross-validation, the technique achieved high accuracy of $$92.41~\%$$ on tissue microarray cores and $$95.42~\%$$ on full tissue sections. We also found that the two boosting algorithms (cw-Boost and AdaBoost.M1) perform consistently well in comparison with other popularly adopted machine learning methods, including support vector machine, neural network and decision tree. This approach offers a robust, objective and rapid procedure for optimized patient-targeted therapy.