Medicinal Image Classification using Association Regulation Mining with Resolution Tree Algorithm
D. Jeyapriya1, S. Theivasigamani2, R. Velvizhi3, P. Nandhini4
1D. Jayapriya, Department of CSE ,Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
2S. Deivasigamani, Department of CSE, Bharath Institute of Higher Education and Research Chenai, Tamilnadu, India.
3R. Velvizhi, Department of CSE ,Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
4P. Nandhini, Department of CSE, Bharath Institute of Higher Education and Research Chennai, Tamilnadu, India.
Manuscript received on 09 July 2019 | Revised Manuscript received on 21 July 2019 | Manuscript Published on 23 August 2019 | PP: 1196-1197 | Volume-8 Issue-9S3 August 2019 | Retrieval Number: I32620789S319/2019©BEIESP | DOI: 10.35940/ijitee.I3262.0789S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper image mining concepts have been used for the diagnosis of the infected cells from the medical images. It manages the certain information extraction, picture information relationship and different examples which are not unequivocally put away in the pictures. This procedure is an expansion of information mining to picture area. Though the medical images are diagnosed using CT-scan and CAD (computer aided diagnosis) nearly 10-30% of the affected cells are not predicted but using this technique the medical images can be clearly diagnosed.
Keywords: Mining, Algorithm, Limitations
Scope of the Article: Classification