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Healthcare has undergone a transformation due to medical imaging technology, which offers detailed insights into the human body. The proposed model aims to advance the analysis of brain tumors by utilizing a complex method that combines CT and MRI -Scan data into an intuitive Flask-based web application. Pioneer-based registration guarantees accurate alignment of various patient pictures, creating a common coordinate system for thorough anatomical comparisons. We use transfer learning to improve the analytical capabilities of the VGG-19 CNN architecture, allowing for more nuanced analysis. The next step, Image Fusion, uses the complimentary capabilities of data from both CT and MRI scans to maximize the segmentation accuracy. By isolating areas of interest, the Watershed transformation makes it easier to categorize data more precisely. Furthermore, a CNN predicts the existence of brain tumors, expediting the process of diagnosis and treatment, and eventually supporting a patient-centered and effective healthcare paradigm. These developments improve accuracy and accessibility in medical procedures while also streamlining the challenging assessment of brain tumors yielding an accuracy of 92.16%.

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Multi-model Fusion for Prediction and Segmentation of Brain Tumor Using CNNs

  • 章节

  • 发表日期：

    2025年

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  • R. Thilagavathy,
  • M. Gayathri,
  • R. Deepalakshmi,
  • Raghvendra Kumar,
  • Ahmed Alkhayyat

摘要

Healthcare has undergone a transformation due to medical imaging technology, which offers detailed insights into the human body. The proposed model aims to advance the analysis of brain tumors by utilizing a complex method that combines CT and MRI -Scan data into an intuitive Flask-based web application. Pioneer-based registration guarantees accurate alignment of various patient pictures, creating a common coordinate system for thorough anatomical comparisons. We use transfer learning to improve the analytical capabilities of the VGG-19 CNN architecture, allowing for more nuanced analysis. The next step, Image Fusion, uses the complimentary capabilities of data from both CT and MRI scans to maximize the segmentation accuracy. By isolating areas of interest, the Watershed transformation makes it easier to categorize data more precisely. Furthermore, a CNN predicts the existence of brain tumors, expediting the process of diagnosis and treatment, and eventually supporting a patient-centered and effective healthcare paradigm. These developments improve accuracy and accessibility in medical procedures while also streamlining the challenging assessment of brain tumors yielding an accuracy of 92.16%.