Brain tumors are a serious health concern as they can cause significant harm to the patient if left untreated. Early detection is crucial for effective treatment, and imaging techniques like computed tomography (CT) scans and magnetic resonance imaging (MRI) are commonly employed for this purpose. However, these techniques have limitations and may not provide enough information to accurately detect and diagnose brain tumors. In this research, we suggest a new method for identifying brain cancers that combines the advantages of neural networks with the Stationary Wavelet Transform (SWT). SWT is a signal processing technique that allows for multi-resolution analysis of an image, while neural networks can learn and classify patterns in data. By fusing the output of SWT with MRI and CT scans, we aim to enhance the visibility of brain tumors and improve their detection accuracy.