Early Detection and Clustering of Lung Cancer in X-Ray Images through Fuzzy - ART Nueral Ntwork
M V Sudhamani, G T Raju
Journal of Information Organization, Vol. 3, No. 4, 2013.
DOI: 
Link: http://www.dline.info/jio/fulltext/v3n4/4.pdf
Abstract:
Cancer is the most familiar disease that affects human body. The time factor is very important to discover the abnormality issues in target patients, especially in various cancer tumors such as lung cancer, breast cancer etc., To increase the survival rate of cancer patients, as it is extremely poor, recently, image processing techniques have been used widely for earlier detection and treatment with reduced risk. This paper presents a Fuzzy-Adaptive Resonance Theory (ART) neural network based approach for early detection of lung cancer from raw chest X-ray images. Image quality and accuracy in predicting the presence of nodules and clustering them into one of the four stages of cancer are the core factors of this paper. Removal of noise using various filters and segmentation of the lung to detect abnormal regions in the X-ray images has been carried out. Relying on extracted features like area, perimeter, shape of the detected nodules, a clustering method is used to cluster the nodules appropriately to verify whether a region is a malignant nodule or not in consent with the domain expert knowledge.