Overview

Brain tumors provide considerable obstacles in the field of healthcare, requiring accurate and prompt diagnosis in order to achieve effective therapy and enhance patient outcomes. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are crucial techniques for identifying brain tumors, with each method providing unique benefits. However, depending exclusively on one modality can restrict the precision of diagnosis. This project presents a novel method that integrates MRI and CT scans to improve the detection and categorization of brain tumors. By utilizing a 13-layer Convolution Neural Network and image fusion algorithms, our approach seeks to combine the advantages of both modalities, reducing their respective drawbacks. The workflow entails the act of uploading MRI and CT scans onto an interface, where a Convolutional Neural Network (CNN)applies picture fusion algorithm in the backend. The classification outcome reveals the existence, nature, or absence of a tumor. Moreover, the results can be obtained through a website or mobile app, making it easier and more effective for healthcare professionals to diagnose patients.

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