2014/06/18 by I. Laurence Aroquiaraj, K. Thangavel, Aroquiaraj, I. Laurence +1
Computer Science · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Radiomics and Machine Learning in Medical Imaging #cs.CV
paper · pdf · doi:10.48550/arxiv.1406.4770
5 pages, 5 figures
arxiv created 2014/06/18 · openalex publication_date 2014/06/18 · arxiv updated 2014/06/19 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Mass classification of objects is an important area of research and application in a variety of fields. In this paper, we present an efficient computer aided mass classification method in digitized mammograms using Fuzzy K-Nearest Neighbor Equality, which performs benign or malignant classification on region of interest that contains mass. One of the major mammographic characteristics for mass classification is texture. Fuzzy K-Nearest Neighbor Equality exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are Haralick and Run length features. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false positive of malignancies. In this paper proposes a novel Fuzzy K-Nearest Neighbor Equality algorithm for classifying the marked regions into benign and malignant and 94.46 sensitivity,96.81 specificity and 96.52 accuracy is achieved that is very much promising compare to the radiologists' accuracy.