International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 6 (2016) pp 4223-4229 © Research India Publications. http://www.ripublication.com 4223 An enhanced k nearest neighbor method to detecting and classifying MRI lung cancer images for large amount data P. Thamilselvan Research Scholar, Department of Computer Science, Bishop Heber College (Autonomous), Tiruchirappalli, Tamilnadu, India. Dr. J. G. R. Sathiaseelan Head, Department of Computer Science, Bishop Heber College (Autonomous), Tiruchirappalli, Tamilnadu, India. Abstract 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. Keywords: Image mining, Image Classification, Morphological Method, K Nearest Neighbor, MRI lung images, large amount of data, Classification Accuracy Introduction Image mining is the calculation progression of removing implicit nontrivial unknown patterns in large amount of data repository. The traditional image mining algorithms are being modified and applied in different research field such as finance, computer security, web content mining, medical and fault diagnosis. Classification is a one of the predominant task in image mining, it also learns to classify the pattern with the help of training process data. The lung cancer is a system of cancer that has become a substantial reason of worldwide death based on the existing reports [1]. The CAD (Computer Aided Diagnosing) system applications that contain mammography masses [2], coronary artery disease [3], various types of cancer like lung, breast, colon [4-6]. The cause of cancer that rests early identification of cancer is the greatest promising ways to decrease the number of deaths. In order to diagnose medical image modalities such as MRI images, CT (Computed Tomography) images and mammography have been implemented to identify the abnormality images. In this work, we have enhanced k nearest neighbor method for detecting and classifying the magnetic resonance lung cancer images. The k nearest neighbor classification rule is based on the density evaluation based on the distance of nearest neighbors and it is a non-parametric technique which is used for classification. The k nearest neighbor classifier method is implemented to classify the handler’s activity based on the structures. In nearest neighbor classification method, the number of modules and structure selection will be key factors. This method for classifying test examples based on nearest training examples in feature space [7]. Basically Euclidean distance vector is used to calculate the familiarity of the samples. In this work, we concentrate on image classification based on the nearest neighbor method to classify medical images. Image classification is prevalent research area in the field of computer visualization [8-11]. Basically an image classification process can be divided into two category i. e. non parametric and parametric classes. The medical images play a vital role to identify the various types of syndrome in human beings. Particularly, magnetic resonance images will be very useful to identify the formation of cancers in human lung image, brain image and breast image treatments. The evaluation of those images with proper classification methods, it will tell valuable information to neurosurgeons whether the level of the formation of cancer in the human lung is benign and malignant [12]. This research mainly concentrated detecting lung cancer tissues in large amount of MRI image data set and measuring the performance proposed EKNN method based on the distance of nearest neighbor and minimize the processing time. Related Work Sreeparna et al. [13] suggested decision tree method for detecting and classifying the retinal abnormalities. The decision tree method is implemented to hybrid contextual evidence with images acquired from other database images. The abnormalities of the retinal images which have been considered in this study from abnormalities such as micro aneurysm, diabetic retinopathy, age related macular degeneration, arising from diabetic retinopathy, hard exudates and cotton wool spots. The decision tree method is a decision support algorithm that used to divide a collection of cases into homogeneous groups and it has like structure or flow chart.