Published Paper Details:

Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. Image quality and accuracy is the core factors of this research, image quality assessment as well as improvement are depending on the enhancement stage. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented. There are three main components: multichannel super pixel level feature extraction and fusion, kernel sparse representation, and segmentation. In this method, sparse coding and dictionary learning, both part of kernel sparse representation, are implemented in a high-dimensional feature space F with the help of the kernel trick. Fusion system passes information within each decomposition level so that the details of the source image is preserved expressing the artifacts. It is difficult to determine whether narrowing of a spinal canal. In Proposed, Qualitative analysis like Clustering and quantitative analysis like feature extraction and image quality assessment is used to segment the cancer detected portion in lung scanned images. To segment the portion, have to filter out the acquired image based upon the masking methodology. The Clustering function will be applied extracted throughout the filtered image. After filtering and contrast enhancement, image quality assessment (Mean Square Error, Peak Signal to Noise Ratio etc.) is calculated to compare other techniques. After segmenting cancer region, the patient�s caretaker can receive the details through E-mail as report. Finally Accuracy estimation will be done for algorithm efficient level. After result, Feature Extraction and Image quality values are plotted and the message is sent to the caretaker through GSM.