Lung cancer is one of the most common and deadly diseases in the world and detection of lung cancer in its early stage is the key to its cure. The objective of this project is to design a method based on image processing and neural networks for the diagnosis of lung cancer in its early stage. The relative frequency histogram of the lung cancer cells is an important parameter in the diagnosis. Feature extraction and classification is an important component in describing the relative frequency histogram. In this paper, feature extraction is done through the wavelet transform and neural networks are used to perform the classification.
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