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Classification of lung cancer using a low population, high dimensional dataset is challenging due to insufficient samples to learn an accurate mapping among features and class labels. Current literature usually handles this task through hand-crafted feature creation and selection. In recent years, deep learning is found to be able to identify the underlying structure of data through the use of autoencoders...
Lung cancer is one among the major causes of cancer related deaths. Fortunately, an early stage diagnosis can increase the survival rates of the patients. Sputum cytology is one of the easiest and cost-effective method for lung cancer diagnosis. Chances of misdiagnosis and sampling error related to sputum cytology led to the concept of malignancy associated changes. Malignancy associated changes (MAC)...
In this project, we assessed the clinical value of tumor heterogeneity measured with 18F-FLT as a biomarker for lung cancer diagnosis and staging, then compared its performance to traditional image features using final pathology as gold standard. We also proposed to apply support vector machine (SVM) to train a vector of image features including heterogeneity extracted from PET image and CT texture...
Two of the most challenging problems in data mining are working with imbalanced datasets and with datasets which have a large number of attributes. In this study we compare three different approaches for handling both class imbalance and high dimensionality simultaneously. The first approach consists of sampling followed by feature selection, with the training data being built using the selected features...
In response to the ICMLA 2009 "Functional Clustering of Gene Expression Profiles in Human Cancers Challenge", we present a new dimension reduction approach that ranks features based on their localized discriminative power. The proposed method is based on a localized dimension reduction penalty added to the objective function for training a hyper basis function (hyper BF or generalized RBF)...
Recent researches have investigated the impact of feature selection methods on the performance of support vector machine (SVM) and claimed that no feature selection methods improve it in high dimension. However, they have based this argument on their experiments with simulated data. We have taken this claim as a research issue and investigated different feature selection methods on the real time micro...
Respiratory gated radiotherapy for lung cancer allows for more precise delivery of prescribed radiation dose to the tumor, while minimizing normal tissue complications. Techniques for fluoroscopic gating without implanted fiducial markers have been developed in a classification framework. Due to the high-dimensionality nature of the images, dimensionality reduction techniques such as principal component...
According to feature extraction of high order cumulant, a new method of detecting lung cancer is proposed applying support vector machine model to recognize the mixed volatile organic compound (VOC) infrared spectrum, where the primary and secondary absorbed peaks are seriously overlapped. The number of spectrum channel of the original spectrum data is large; hence, the transmitted spectrogram is...
Accurate lung tumor targeting in real time plays a fundamental role in image-guide radiotherapy of lung cancers. Precise tumor targeting is required for both respiratory gating and tracking. Gating is considered as the current state of the art for precise lung cancer radiotherapy, which irradiates the tumor when it moves into a predefined gating window. Tracking seems to be a next-generation technique,...
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