Aim: The goal of the work is to compare the Discrete Wavelet Transform (DWT) of multimodal medical image fusion with enhanced correlation and joint entropy by utilizing the innovative maximum fusion rule and average fusion rule. Materials and Methods: Brain tumor images are taken from the Kaggle website. The samples are separated into two categories. Each group has a sample size of 10 images. Using parameter values from previous iterations, clinical.com was used to calculate sample size. The pretest G power is set at 80% and the confidence interval is set at 95%. The DWT is estimated with the standard dataset and MATLAB programming. Results: Comparison of DWT is done by independent sample t-test using SPSS software. There is a statistical significant difference between innovative maximum fusion rule and average fusion rule with p=0.044 (p<0.05) showed better results in comparison to maximum algorithm. Conclusion: Maximum fusion rule algorithm of DWT values found to show better results than an average fusion rule algorithm for the detection of brain tumor.