Oral Cancer Detection: Modified KFCM Segmentation Clustering Algorithm
Keywords:
Oral and maxillofacial surgery, Image Pre-processing, segmentation of KFCM, Operation of morphologicalAbstract
Neoplasm of the mouth is one of the world's life-threatening diseases. It grows in the oral cavity and nasopharynx as a malevolent neoplasm. It is part of a wider group of cancers called cancer of the head and neck. They mostly form in the mouth, tongue, cheeks, hard and soft palate, sinuses, lips and pharynx in squamous cells (throat). Because of the unregulated growth of abnormal cells, the mortality rate is on the increase. In order to minimize the probability of death, early detection and diagnosis of oral cancer are essential. MRI images Lesion is an innovative medical concept. MRI images reflect an inventive concept. An oral malignancy may typically be measured by the use of an MRI scan or a CT scan. The scale and extent of oral cancer spreading can be seen in MRIs. The distinction between normal and abnormal tissue can be seen. MRI scan is a pioneering tool for oral cancer diagnosis. The method for studying the advancement of oral malignancy is non-invasive, radiation-free imaging. The present article compares modified KFCM to the K-means and Fuzzy C-means (FCM) and analyzes the precision achieved with the segmentation. The K-Means algorithm is used as a clustering technique to eliminate the calibration time. The FCM algorithm is used to minimize the total iteration generated by the initialization of the exact cluster. The morphological operation is used to excerpt the appropriate area from the FCM cluster. The lesion area is finally calculated. The proposed approach focused on modified anisotropic diffused filter image pre-processing to remove artifacts from MRI images and segmentation techniques using KFCM clustering, segmentation, lesion removal from MRI and the exact region of lesion evaluation. The modified KFCM algorithm is used in this research to increase segmentation precision (accuracy).
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