Oral Cancer Detection: Hybrid Method of KFCM Clustering
Shilpa Harnale1, Dhananjay Maktedar2
1Shilpa Harnale , CSE department, BKIT BHALKI-India.
2Dr Dhananjay Maktedar, CSE department, GNDEC Bidar- India.

Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2287-2292 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5797018520/2020©BEIESP | DOI: 10.35940/ijrte.E5797.018520

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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: Oral neoplasm is one of the complex diseases in the world. The risk of death is increasing all over the world due to the rapid swelling of abnormal tissues. Early diagnosis of malignancy is necessary to avoid the risk of death. The tumor detected from magnetic resonance imaging (MRI) images is new innovative analysis topic in medical intervention. Normally the internal structure of the mouth can be examined using the MRI scan or CT scan. MRI scan is an advantageous and adequate technique for detection of the oral malignancy. It is non-invasive because it does not use any radiation. In this study, the hybrid approach KFCM is proposed for the segmentation and compared with conventional K-Means & Fuzzy C-Means(FCM).The main objective of merging these two algorithms is to reduce the total iterations generated by initializing an exact cluster to the FCM clustering with less computation time. The developed system concentrated on image enhancement using anisotropic diffusion to improve the quality of image and segmentation technique using KFCM clustering to reduce computation time &improve the segmentation accuracy. It exactly segments the lesion region and evaluates the lesion area.
Keywords: Oral and Maxillofacial Surgery, Pre-Processing, KFCM Clustering, Morphological Operations.
Scope of the Article: Network Operations & Management.