An enhanced k nearest neighbor method to detecting and classifying MRI lung cancer images for large amount data

The k nearest neighbor classification method is one of the humblest method in conceptually and it is a top method in image mining. In this work, the enhanced k nearest neighbor (EKNN) technique has been implemented to identify the cancer and automatic classification of benign and malignant tissues in the huge amount of lung cancer image datasets. In this proposed system, we have used three stages such as preprocessing, identification of cancer and classification. In preprocessing phase, the morphological method has been used to improve the quality of images. We used enhanced k nearest neighbor (EKNN) classifier for identifying the cancer and classifying the images. The classification is done by implementing four steps of k nearest neighbor which are calculated based on Euclidean distance, define the k value, assigning majority class and finding the minimum distance. The nodule identification and classification of process trained and tested on large-scale image databases.