Abstract: In our daily life, cancer is well-known disease that causes of death in both men and women and understand about the survival rate of lung cancer which is extremely poor. To increase this survival rate of cancerous patient, it is primarily to detect at premature stage which enables many new options for the cancer treatment without risk. In this paper, we represent Lung Cancer Detection System for finding of lung cancer with the help of image processing mechanisms. This paper presents a neural network based approach to detect lung cancer from raw chest images. These extracted features are considered as the inputs of neural network to train and to verify whether the extracted noduleis a malignant or non-malignant. This research work concentrate on detecting nodules, early stages of cancer diseases, appearing in patient’s lungs. Most of the nodules can be observed after carefully selection of parameters. The training dataset of CT images are processed in three stages to attain more quality and accuracy in the processed examination. For segmentation purpose FCM technique is used.
Keywords: probabilistic neural network, image processing, segmentation, Fuzzy c-means clustering, lung tumor.
Title: Feature Based Detection of Lung Tumor Using Fuzzyc-Means Clustering and Classifying Using Probabilistic Neural Networks
Author: Dr. P.V.Ramaraju, G.Nagaraju, K.Poojitha, K.B.V.S.P.Babu, Ch.Balasubramanyam,
International Journal of Electrical and Electronics Research
ISSN 2348-6988 (online)
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