Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
A Novel Deep Learning Approach for Brain Tumor Detection

A Novel Deep Learning Approach for Brain Tumor Detection

R, Bhavani
R, Subraja
T, Malathy

Paperback

Medical Reference

ISBN10: 6207842170
ISBN13: 9786207842179
Publisher: LAP Lambert Academic Publishing
Published: Jul 15 2024
Pages: 76
Weight: 0.27
Height: 0.18 Width: 6.00 Depth: 9.00
Language: English
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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