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Articles

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

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References

  • Available: https://www.wcrf.org/cancer/cancer-trends/skin-cancer-statistics
  • Available: https://www.ncbi.nlm.gov/books
  • F. Ercal, A. Chawla, W. V. Stoecker, H. C. Lee, and R. H. Moss, “Neural network diagnosis of melanoma from color images,” IEEE Trans., Vol. 41, no. 9, pp. 837–45, 1994.
  • T. Kekre, G. Saylee, and R. Kavita, “Detection of cancer using vector quantization for segmentation,” Int. J. Comput. Appl., Vol. 4, no. 9, pp. 14–9, 2010.
  • J. Chakraborty, S. Mukhopadhyay, V. Singla, N. Khandelwal, and P. Bhattacharyya, “Automatic detection of pectoral muscle using average gradient and shape based feature,”J. Digit. Imaging, Vol. 25, no. 3, pp. 387–99, 2012.
  • T. Y. Satheesha, D. Satyanarayana, M. G. Prasad, and K. D. Dhruve, “Melanoma is skin deep: A 3D reconstruction technique for computerized dermoscopic skin lesion classification,” IEEE. J. Transl. Eng. Health. Med., Vol. 5, pp. 1–17, 2017.
  • G. Arora, A. K. Dubey, and Z. A. Jaffery, “Design of Dmey wavelet Gaussian filter (DWGF) for de-noising of skin lesion images,” in Smart Innovations in Communication and Computational Sciences, Springer: Singapore, 2019, pp. 475–84.
  • C. Gupta, N. K. Gondhi, and P. K. Lehana, “Analysis and identification of dermatological diseases using mixture modeling,” IEEE Access., Vol. 7, pp. 99407–427, 2019.
  • I. Bhakta, S. Phadikar, K. Majumder, A. Sau, and S. Chowdhuri, “Tsalli’s entropy-based segmentation method for accurate pigmented skin lesion identification,” IETE J. Res., 1–17, 2019.
  • S. Alheejawi, H. Xu, R. Berendt, N. Jha, and M. Mandal, “Novel lymph node segmentation and proliferation index measurement for skin melanoma biopsy images,” Comput. Med. Imaging Graph., Vol. 73, pp. 19–29, 2019.
  • T. Saba, M. A. Khan, A. Rehman, and S. L. Marie-Sainte, “Region extraction and classification of skin cancer: a heterogeneous framework of Deep CNN features fusion and reduction,” J. Med. Syst., Vol. 43, no. 9, pp. 289, 2019.
  • T. Sreelatha, M. V. Subramanyam, and M. G. Prasad, “Early detection of skin cancer using melanoma segmentation technique,” J. Med. Syst., Vol. 43, no. 7, pp. 190, 2019.
  • E. A. AlMansour, M. A. Jaffarb, and S. C. AlMansour, “Fuzzy contour based automatic segmentation of skin lesions in dermoscopic images,” Int. J. Netw. Secur, Vol. 17, no. 1, pp. 177, 2017.
  • L. Bi, J. Kim, E. Ahn, D. Feng, and M. Fulham, “Automated skin lesion segmentation via image-wise supervised learning and multi-scale superpixel based cellular automata,” in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016, pp. 1059–62.
  • F. Liao, M. Liang, Z. Li, X. Hu, and S. Song, “Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-or network,” IEEE Trans. Neural Netw. Learn. Syst., Vol. 30, no. 11, pp. 3484–95, 2019.
  • P. Tang, Q. Liang, X. Yan, S. Xiang, W. Sun, D. Zhang, and G. Coppola, “Efficient skin lesion segmentation using separable-Unet with stochastic weight averaging,” Comput. Methods Programs Biomed., Vol. 178, pp. 289–301, 2019.
  • F. F. X. Vasconcelos, A. G. Medeiros, S. A. Peixoto, and P. P. Reboucas Filho, “Automatic skin lesions segmentation based on a new morphological approach via geodesic active contour,” Cogn. Syst. Res., Vol. 55, pp. 44–59, 2019.
  • Available: https://isic-archive.com
  • M. Younes, and K. Salman, “Contrast improvement of focal liver lesions using a new Histogram equalization,” in Fundamental research in EE, Singapore: Springer, 2019, pp. 43–53.
  • R. B. Dubey, M. Hanmandlu, S. K. Gupta, and S. K. Gupta, “Region growing for MRI brain tumor volume analysis,” Indian J. Sci. Technol., Vol. 2, no. 9, pp. 26–31, 2009.
  • L. Grady, “Random walks for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 28, no. 11, pp. 1768–83, 2006. doi:10.1109/TPAMI.2006.233.
  • E. Ahn, J. Kim, L. Bi, A. Kumar, C. Li, M. Fulham, and D. D. Feng, “Saliency-based lesion segmentation via background detection in dermoscopic images,” IEEE. J. Biomed. Health Inform., Vol. 21, pp. 1685–93, 2017.
  • Y. Yuan, M. Chao, and Y. C. Lo, “Automatic skin lesion segmentation using deep fully convolutional networks With jaccard distance,” IEEE Trans. Med. Imaging, Vol. 36, pp. 1876–86, 2017.
  • J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431–40.

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