An Efficient Cancer Classification Model for CT/MRI/PET Fused Images

Objective: Image fusion-based cancer classification models used to diagnose cancer and assessment of medical problems in earlier stages that help doctors or health care professionals to plan the treatment plan accordingly. Methods : In this work, a novel Curvelet transform-based image fusion method is developed. CT and PET scan images of benign type tumors fused together using the proposed fusion algorithm and the same way MRI and PET scan images of malignant type tumors fused together to achieve the combined benefits of individual imaging techniques. Then the Marker controlled watershed Algorithm applied on fused image to segment cancer affected area. The various color features, shape features and texture-based features extracted from the segmented image. Then a data set formed with various features, which have given as input to different classifiers namely neural network classifier, Random forest classifier, K-NN classifier to determine the nature of cancer. The results of the classifier will be Normal, Benign or Malignant category of cancer. Results: The performance of the proposed fusion Algorithm compared with existing fusion techniques based on the parameters PSNR, SSIM, Entropy, Mean and Standard Deviation. Curvelet transform based fusion method performs better than already existing methods in terms of five parameters. The performances of classifiers are evaluated using three parameters Accuracy, Sensitivity, and Specificity. K-NN Classifier performs better when compared to the other two classifiers and it provides overall accuracy of 94%, Sensitivity of 88% and Specificity of 84%. Conclusion: The proposed Curvelet transform based image fusion method combined with the K-NN classifier provides better results when compared to other two classifiers when two input images used individually.