Computed-Aided Detection In Chest X-Ray

The lung cancer is the No. 1 Cancer Killer worldwide, and the cure of tumors is always the aim that people pursued. The key of treatments is early diagnosis. Being regarded as one of the general clinical examination, chest radiography is ubiquitous in clinical practice, it make early diagnosis of primary lung cancer significant. But at the same time it encountered many problems, for example radiographer often missed tumors and diagnosis falsely when they faced so many images. The techniques of image processing and pattern recognition make it possible to computer-aided read images, it helps doctors to increase the workflow efficiency and reduce their eye fatigue, thus may improve cancer diagnostic accuracy. The detection of lung nodule includes pre-processing of images, the extraction of pulmonary parenchyma, the segmentation of region of interest (ROI), features extraction of ROI and classification. The performance is bad when we use traditional classification algorithms, because the number of normal samples is far more than the number of cancer samples. Therefore, it is very important to research on classification on imbalanced data.We do research from there aspects: Firstly, we propose a novel over-sampling approach named LLE-Based SMOTE. It can reduce the imbalance of data through incorporating over-sampling method with nonlinear dimensional reduction. The locally linear embedding algorithm (LLE) is first applied to map the high-dimensional data into a low-dimensional space, where the input data is more separable, and thus can be over-sampled by synthetic minority over-sampling technique (SMOTE).Then the synthetic data points generated by SMOTE are mapped back to the original input space as well through LLE. Secondly, we introduce support vector machines with different costs (SDC), which uses different errors for different class. Besides, we apply sequential minimal optimization (SMO) to solving the...