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Skin tumor acts as a premier factor for high death rate throughout the world. It is difficult for the radiologists to segment the skin tumour cells. Various research work focus on accurate segmentation but not on the time of processing. The intention of this research work is to provide an efficient enhancement method and tumor detection from other unaltered regions. This work relies mainly on computed tomography (CT) tumor images of the skin, benign or malignant, that has been implemented efficiently. In this research paper, a novel methodology called Online Tiger Claw Region Based Segmentation (OTCRBS) is proposed which is used mainly to detect the boundary of unaffected Skin Cell, similar to tiger which uses its claws to tear off the skin of its prey during the search for its food. By using metric for the region, various properties can be formulated for the detection of anomalous skin cells. 98.68 and 97.71% accuracy is produced for procurement of benign and malignant nodule in MATLAB 2018a, respectively. Computation time was only 7.65 s. Comparative analysis is made with different segmentation methods. Experimental results establish that the proposed flow outperforms all the existing segmentation methods for the proper detection of tumor cells.

Automatic Skin Tumor Detection Using Online Tiger Claw Region Based Segmentation – A Novel Comparative Technique