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The objective of this paper is to classify likely cancerous and noncancerous lung image and to detect the location of the nodule in the lung image provided by CT scan. The novelness of this paper is to provide better accuracy and assists radiologist to analyze CT scan images of lung accurately. This efficient proposed method consists of image enhancement, extracting region of interest using Active Contour Model, extracting spatial features from segmented image, train those feature vectors and classify the test image through Fuzzy Inference System. This proposed method performance is compared with one of the most efficient and popular existing method Support Vector Machine and shows better accuracy of 94.12% Keywords-Active ContourModel;Fuzzy Inference System;Support Vector Machine;image enhancement or preprocessing I.

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The content of web is increasing rapidly and search engines are used to search the web content. Along with this, it is also becoming a point of focus for academic research. Computer programs are needed in order to conduct any processing of web pages. Many web search engines as well as many more specialized search tools rely on web crawlers to acquire large collection of pages for indexing and analysis. Crawlers follow the hyperlinks in web pages to automatically download new and updated web pages. With the outbreak in diseases and increase in the number of people affected by it, there was a high need to make the process of treatment quite flexible through the combination of medicine and internet. In this paper, we describe the design and implementation of medical search engine which is used to fetch the web content related to medical field. It is an innovation to help the people to readily search their nearby hospitals and utilize the best treatment as soon as possible. The project will bring disease description and possible hospitals, NGOs together on a common platform. It will be an easy effort to search for the best effective treatment centre through internet.