Publication Date: 2024/02/05
Abstract: In order to improve brain tumour analysis, our research uses MRI and CT data in a Flask-based web application. Our research focuses on advancing brain tumor analysis through a sophisticated approach that integrates MRI and CT data within a user-friendly Flask-based web application. The landmark-based registration ensures precise alignment of diverse patient images, establishing a standardized coordinate system for meticulous anatomical comparisons. To enhance the VGG-19 CNN architecture's analytical capabilities, we employ transfer learning, enabling nuanced analysis. The subsequent Image Fusion process optimizes tumor segmentation accuracy by leveraging the complementary strengths of CT and MRI data. The Watershed transformation isolates regions of interest, facilitating a more refined segmentation process. Additionally, a CNN predicts the presence of brain tumors, streamlining detection and prognosis, ultimately contributing to a healthcare paradigm that is both efficient and patient- centered. These advancements not only streamline the intricate examination of brain tumors but also enhance accessibility and accuracy in healthcare practices.
Keywords: CNN, Flask, VGG -19, Image Fusion, Watershed Transformation.
DOI: https://doi.org/10.5281/zenodo.10618098
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN1887.pdf
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