Notice of Violation of IEEE Publication Principles - A framework for medical image classification using soft set

Notice of Violation of IEEE Publication Principles "A Framework for Medical Image Classification Using Soft Set" by N.K. Anitha, G. Keerthika, M.Maheswari, J. Praveena in the Proceedings of the 2nd International Conference on Current Trends in Engineering and Technology (ICCTET), July 2014, pp. 268-272 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: "A Framework for Medical Images Classification Using Soft Set" by Saima Anwar Lashari, Rosziati Ibraham in Procedia Technology 11, 2013, pp. 548-556 Medical image classification is a significant research area that receives growing attention from both the research community and medicine industry. It addresses the problem of diagnosis, analysis and teaching purposes in medicine. For these several medical imaging modalities and applications based on data mining techniques have been proposed and developed. Thus, the primary objective of medical image classification is not only to achieve good accuracy but to understand which parts of anatomy are affected by the disease to help clinicians in early diagnosis of the pathology and in learning the progression of a disease. This furnishes motivation from the advancement in data mining techniques and particularly in soft set, to propose a classification algorithm based on the notions of soft set theory. As a result, a new framework for medical imaging classification consisting of six phases namely: data acquisition, data pre-processing, data partition, soft set classifier, data analysis and performance evolution is presented.