Artificial neural network classification-based skin cancer detection

  • Abstract
  • Keywords
  • References
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  • Abstract

    At present, skin cancers are extremely the most severe and life-threatening kind of cancer. The majority of the pores and skin cancers are completely remediable at premature periods. Therefore, a premature recognition of pores and skin cancer can effectively protect the patients. Due to the progress of modern technology, premature recognition is very easy to identify. It is not extremely complicated to discover the affected pores and skin cancers with the exploitation of Artificial Neural Network (ANN). The treatment procedure exploits image processing strategies and Artificial Intelligence. It must be noted that, the dermoscopy photograph of pores and skin cancer is effectively determined and it is processed to several pre-processing for the purpose of noise eradication and enrichment in image quality. Subsequently, the photograph is distributed through image segmentation by means of thresholding. Few components distinctive for skin most cancers regions. These features are mined the practice of function extraction scheme - 2D Wavelet Transform scheme. These outcomes are provides to the Back-Propagation Neural (BPN) Network for effective classification. This completely categorizes the data set into either cancerous or non-cancerous. 

  • Keywords

    : Skin Cancer, Artificial Neural Network, Segmentation, Wavelet Transform, Back Propagation.

  • References

      [1] Lau HT & Al-Jumaily A, “Automatically early detection of skin cancer: Study based on neural network classification”, International Conference of Soft Computing and Pattern Recognition, (2009), pp.375-380.

      [2] Ercal F, Chawla A, Stoecker WV, Lee HC & Moss RH, “Neural network diagnosis of malignant melanoma from color images”, IEEE Transactions on biomedical engineering, Vol.41, No.9,(1994), pp.837-845.

      [3] Ali AH & Kabir MH, “Wavelets Pre-Processing of Artificial Neural Networks Classifiers”, IEEE Transactions on Consumer Electronics, Vol.53, No.2, (2008), pp.593-600.

      [4] Agrawal P, Shriwastava SK & Limaye SS, “MATLAB implementation of image segmentation algorithms”, IEEE Pacic Rim Conference on Communication, Computer and Signal Processing, (2010), pp. 602-605.

      [5] Shahbahrami A & Tang J, “A Colour Image Segmentation algorithm Based on Region Growing”, IEEE Trans. on Consumer Electronics Euromicro, (2010), pp.362-368.

      [6] Tanaka T, Yamada R, Tanaka M, Shimizu K & Tanaka M, “A Study on the Image Diagnosis of Melanoma”, IEEE Trans. on Image Processing, (2004), pp.1010-1024.

      [7] Andreopoulos Y, Zervas ND, Lafruit G & Schelkens P, “A local wavelet transform implementation versus an optimal row-column algorithm for the 2D multilevel decomposition”, IEEE International Conf. on Image Processing, Vol.3, (2001).

      [8] Sheha MA, Mabrouk MS & Sharawy A, “Automatic detection of melanoma skin cancer using texture analysis”, International Journal of Computer Applications, Vol.42, No.20,(2012), pp.22-26.

      [9] Vetterli M & Riol O, “Wavelets and Signal processing”, IEEE Signal Processing Magazine, (1991), pp.14-38.

      [10] Ray S & Chan A, “Automatic feature extraction from wavelet coefficients using Genetic Algorithms”, IEEE Trans. on Image Processing, (2000), pp. 980-1025.




Article ID: 10364
DOI: 10.14419/ijet.v7i1.1.10364

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