Automatic detection of white blood cells cancer diseases

Electronics and Communication Engineering

Blood smear images are efficient and reliable source of information in extraction of features in case of cancer detection. In assessment of cancer detection like leukemia cancer image feature extraction and reduction has to be carried out efficiently. It discusses a robust image feature extraction algorithm using scale invariant feature points for key point extraction and GLCM Shape Analysis. For matching the key points, k nearest network algorithm is developed with tumbling functions of model. This technique assists in comparing the scale invariant feature points and GLCM features. From the obtained outcomes, the capability of the obtained accuracy is estimated and it is wrapped up that the proposed approach is efficient in regards to SVM algorithm.

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