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An End-to-end Process for Cancer Identification from Images of Lung Tissue By Daniel Wayne McKee
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Doctoral Dissertation. The purpose of this study was to develop a non-interactive, computer-based second opinion diagnostic tool that could read microscope images of lung tissue and classify the... More > tissue sample as normal or cancerous. This problem can be broken down into three areas: segmentation, feature extraction and measurement, and classification. This study introduces a kernel-based extension of fuzzy c-means to provide a coarse initial segmentation, with heuristically-based mechanisms to improve the accuracy of the segmentation. The segmented image is then processed to extract and quantify features. Finally, the measured features are used by a Support Vector Machine (SVM) to classify the tissue sample. The performance of this approach was tested using a database of 83 images collected at the Moffitt Cancer Center and Research Institute. These images represent a wide variety of normal lung tissue samples, as well as multiple types of lung cancer.< Less

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