Leukaemia Detection in Microscopic Imagery using Optimization Algorithm

  • Authors

    • M Venkata Dasu
    • P Subbaiah
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.12068
  • Leukemia CLAHE, Segmentation, Cuckoo Search(CSO), Ant Colony optimization(ACO), SIFT.

  • In this paper, automated approach of blood cancer detection is proposed. Usually microscopic images examined by experts manually are time consuming and less accuracy. The automated blood cancer detection system analyses the microscopic image and overcomes these drawbacks. The proposed system extracts the features of the image and applies filtering techniques. In this paper proposed method is cuckoo search optimization algorithm which is used in line with segmentation. The features of segmented image can be obtained from Scale invariant feature transform. Some of the features like PSNR, sensitivity, accuracy, etc. are calculated for leukemia detection. The performance is compared with the existing method ACO. The proposed system is tested on image dataset and 94.24% accuracy is achieved. The proposed system is successfully implemented in MATLAB.

     

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    Venkata Dasu, M., & Subbaiah, P. (2018). Leukaemia Detection in Microscopic Imagery using Optimization Algorithm. International Journal of Engineering & Technology, 7(2.24), 299-303. https://doi.org/10.14419/ijet.v7i2.24.12068