This document summarizes a research paper that proposes a method for detecting brain tumors by fusing CT and MRI images using stationary wavelet transform and a probabilistic neural network classifier. The proposed method involves preprocessing the CT and MRI images using median filtering for noise removal. It then applies stationary wavelet transform to the images to extract features before segmenting the tumor region using k-means clustering. Finally, the probabilistic neural network classifier determines if the tumor is benign or malignant based on the fused image features. The paper reviews other existing fusion and classification methods and argues that the proposed stationary wavelet transform and probabilistic neural network approach provides better detection of brain tumors.