PROPOSED METHODLung is an important organ in human body that performs vital functions for every second. Hence we need to consider the main lung abnormalities in detection, diagnosis and also treatment of detected abnormality if it is possible. Also lung diseases are said to be the main reason for death around the world. The main reasons behind the lung diseases are inhaling chemicals, inhaling dusts , or even due to bacterial action. According to a survey done on Indian people approximately 70275 were affected by major lung diseases , among which 63759 people were not in a condition to be curedand caused their deaths.
We propose a novel method to detect lung abnormalities in a efficient manner. The parameters are evaluated for feature extraction are Haarlick features, No.of black pixels, grid features and haar wavelets, For classification hybrid feed forward network with nave bayes classifier is used.MODELINGTo detect the lung cancer from the given image a novel classification method is used. Initially the image is preprocessed using wiener filter.
Then the image features are extracted. Then the extracted features are given into the classifier inorder to detect whether the patient affected by cancer or not.PRIMARY OBJECTIVE To detect the lung cancer in a efficient manner. To extract the features of the image effectively. To improve the classification accuracy. To give the noise free solution.PERFORMANCE MEASUREMENTThe performance of the proposed method is evaluated based on the following factors: Accuracy Mean square error Sensitivity SpecificityCONCLUSION In this paper we survey different techniques for lung cancer detection system. Detecting cancer at early stage prove to be vital as the mortality rate is abruptly increasing annually. Lung cancer can be detected by identifying affected nodules at early stage. This paper has given a brief review on recent developments in lung cancer detection methods. Various techniques have been used in the lung cancer detection methods to improve the efficiency of cancer detection. 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