Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
C4.5: Programs for Machine Learning
C4.5: Programs for Machine Learning
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
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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 classification system for real time classification of AC and SC based on morphological tissue pattern of H&E images alone to assist medical experts in 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, 369 tissue samples were collected in tissue microarray format, including 97 adenocarcinoma and 272 squamous carcinoma samples. Using 10-fold cross validation, the technique achieved high accuracy of 92.41%, and 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, single decision tree and alternative decision tree. This approach offers a robust, objective and rapid procedure for optimized patient-targeted therapies.