Design and Analysis of High Gain Modified Sepic Converter for Photovoltaic Applications


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The liver cancer rate is increasing year by year. Generally, liver cancer?s death rate is very high because the disease causes no symptoms, so it?s often not caught until it’s in final stages. We propose methods for liver cancer detection which is based on concepts of neural network. Wavelet and glcm features extracted from input image. This glcm features classify to dataset and then get result is normal or abnormal stages. Many techniques have been developed for the detection of liver tumor utilizing the aberrant lesion size and shape. The novel methodology for the detection and diagnosis of liver tumor is additionally proposed in this paper for the detection and diagnosis of liver tumor.

Existing Method

  • Principal Component Analysis
  • DCT ?and shape features
  • KNN and SVM classifier

Draw backs of Existing method

  • High Computational load and poor discriminatory power.
  • SVM is slow training for large feature set.
  • Less accuracy in classification

Proposed Method

  • DTCWT and GLCM Features
  • NN Classifier
  • K-means Clustering


  • The segmentation algorithm Proves to be simple and effective
  • Grey scale Co-occurrence matrix performed well in NN
  • Better texture and edge representation
  • Segmentation provides better clustering efficiency


  • Computer aided Diagnosis system for interstitial lung diseases in medical Application

Software Requirement

  • MATLAB 2014a


  • [1] Upadhyay, Y. and Wasson, V. 2014. “Analysis of Liver MR Images for Cancer Detection using Genetic Algorithm”. International Journal of Engineering Research and General Science. Vol.2, No.4, PP: 730-737.
  • [2] Kumar, P. Bhalerao, S. 2014. “Detection of Tumor in Liver Using Image Segmentation and Registration Technique”. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE). Vo.9, No.2, PP: 110-115.
  • [3] Selle, D.; Spindler, W.; Preim, B. and Peitgen, H. O. 2000. “Mathematical Methods in Medical Imaging: Analysis of Vascular Structures for Liver Surgery Planning”. PP: 1-21.
  • [4] Zimmer, C. and Olivo-Marin, J. C. 2005. “Coupled Parametric Active Contours”. Transactions on pattern Analysis and Machine Intelligence. Vol.27, No.11, PP: 1838-1841.

[5] Chitra, S. and Balakrishnan, G. 2012. “Comparative Study for Two Color Spaces HSCbCr and YCbCr in Skin Color Detection”. Applied Mathematical Sciences. Vol.6, No.85, PP: 4229 ? 4238

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