The automated Computer Aided Diagnosing (CAD) system is proposed in this paper for detection of lung cancer form the analysis of computed tomography images. In recent years the image processing mechanisms are used widely in several medical areas for improving earlier detection and treatment stages, in which the time factor is very important to discover the disease in the patient as possible as fast, especially in various cancer types such as the lung cancer, breast cancer. Lung cancer is the second most commonly diagnosed cancer in the United States, and it is the leading cancer related death in the world, with the current fatality rate exceeding that of the next three most common cancers (breast, prostate, and colorectal) combined. In this research, we considered the problem of developing an automated system for detecting the presence of pulmonary nodules in the lung CT .The essence of developing a system like that needed to focus on detecting nodules in their early stages, which are the very small nodules that are likely to be overlooked by the radiologists. This paper involves cancer detection system based on texture features extracted from the slice of DICOM Lung CT images for the identification of cancerous nodules. In developing this system we passed the available lung CT images and its database in basic three stages to achieve more accuracy in our experimental results: Firstly A pre-processing stage involving some image enhancement techniques helps to solve the problem. We preprocess the images (by contrast enhancement, thresholding, filtering, and blob analysis) obtained after scanning the Lung CT Images and secondly separate the suspected nodule areas (SNA) from the image by a segmentation process by using thresholding segmentation mechanism by Otsu thresholding algorithm and region growing techniques. Finally we relied on Texture features which help us to make a comparison between cancerous and non cancerous images. For accurate detection of cancerous nodules, we need to differentiate the cancerous nodules from the noncancerous. We developed an artificial neural network to differentiate them. We trained the neural network by the backpropagation algorithm and tested it with different images from a database of the DICOM CT Lung images of NIH/NCI Lung Image Database Consortium (LIDC) dataset.

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Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (precision) and linguistic representation (interpretability). Fuzzy modeling-meaning the construction of fuzzy systems-is an arduous task, demanding the identification of many parameters. This paper analyses the fuzzy-modeling problem and different approaches to coping with it, focusing on evolutionary fuzzy modeling-the design of fuzzy inference systems using evolutionary algorithms. The purpose of this paper is twofold. We first provide an overview of the standard approach to constructing a fuzzy control system and then identify a wide variety of relevant system modeling techniques. The later part of the paper deals with discussing Fuzzy modeling problem -curse of dimensionality and techniques to solve the problem. The paper provides an introduction to the use of fuzzy sets and fuzzy logic for the approximation of functions and modeling of static and dynamic systems. The concept of a fuzzy system is first explained. Afterwards, the motivation and practical relevance of fuzzy modeling are highlighted.

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