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Automatic Skin Tumor Detection Using Online Tiger Claw Region Based Segmentation – A Novel Comparative Technique
IETE Journal of Research ( IF 1.5 ) Pub Date : 2021-04-13 , DOI: 10.1080/03772063.2021.1911694
A. Ashwini 1 , V. Kavitha 2
Affiliation  

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.



中文翻译:

使用在线虎爪区域分割进行自动皮肤肿瘤检测——一种新颖的比较技术

皮肤肿瘤是全世界高死亡率的首要因素。放射科医生很难分割皮肤肿瘤细胞。各种研究工作都集中在精确分割上,而不是处理时间上。这项研究工作的目的是提供一种有效的增强方法和从其他未改变区域检测肿瘤的方法。这项工作主要依赖于已有效实施的皮肤良性或恶性计算机断层扫描 (CT) 肿瘤图像。在这篇研究论文中,提出了一种称为在线虎爪区域分割(OTCRBS)的新方法,该方法主要用于检测未受影响的皮肤细胞的边界,类似于老虎在捕获猎物时用爪子撕下猎物的皮肤。寻找它的食物。通过使用该区域的度量,可以制定各种特性来检测异常皮肤细胞。MATLAB 2018a 中良性结节和恶性结节采购的准确率分别为 98.68% 和 97.71%。计算时间仅为 7.65 秒。对不同的分割方法进行了比较分析。实验结果表明,所提出的流程优于所有现有的正确检测肿瘤细胞的分割方法。

更新日期:2021-04-13
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