Lung cancer detection and classification on CT scan images using enhanced artificial bee colony optimization

  • Abstract
  • Keywords
  • References
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  • Abstract

    In recent years, prediction of cancer at earlier stages is obligatory to increase the chance of survival of the afflicted. The most dreadful type is lung cancer, which is identified as one of the most common diseases among humans worldwide. In this research work, the raw input image which usually suffers from noise issues are highly enhanced using Gabor filter image processing. The region of interest from lung cancer images are extracted with Otsu’s threshold segmentation method and 5- level HAAR discrete wavelet transform method which possess maximum speed and high accuracy. The proposed Enhanced Artificial Bee Colony Optimization (EABC) is applied to detect the cancer suspected area in CT (Computed tomography) scan images. The proposed EABC implementation part, utilizes CT (Computed Tomography) scanned lung images with MATLAB software environment. This method can assist radiologists and medicinal experts to recognize the illness of syndromes at primary stages and to evade severe advance stages of cancer.


  • Keywords

    Image Segmentation; Artificial Neural Network; Enhanced Artificial Bee Colony Optimization.

  • References

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Article ID: 12538
DOI: 10.14419/ijet.v7i2.26.12538

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