Integration of Generative AI and Image Fusion for Enhanced Brain Tumor Detection in PET-MRI Data

This study introduces a novel generative AI-driven system for brain tumor detection by integrating PET and MRI data through an advanced image fusion approach. The fused images were used for training on the machine in the real-world dataset. It provided very high sensitivity and specificity with a sensitivity of 98.7 and a specificity of 99.2%. Ablation experiments with statistical analysis demonstrated the superiority of the system along with its robustness with other existing models. The viability of the technology for healthcare applications was also illustrated through real-time implementation through an intuitive interface. This research introduces a novel approach to diagnosing brain malignancies using multimodal imaging to set new benchmarks for accuracy and reliability.

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Publication Details

Published
2025-03-21
DOI
https://doi.org/10.1109/dicct64131.2025.10986386
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
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article

Integration of Generative AI and Image Fusion for Enhanced Brain Tumor Detection in PET-MRI Data

Arulselvi Subramanian, N. Aashna Unnikrishnan, B. Karthik
Brain Tumor Detection and Classification
article

Integration of Generative AI and Image Fusion for Enhanced Brain Tumor Detection in PET-MRI Data

Arulselvi Subramanian, N. Aashna Unnikrishnan, B. Karthik
article en

Abstract

This study introduces a novel generative AI-driven system for brain tumor detection by integrating PET and MRI data through an advanced image fusion approach. The fused images were used for training on the machine in the real-world dataset. It provided very high sensitivity and specificity with a sensitivity of 98.7 and a specificity of 99.2%. Ablation experiments with statistical analysis demonstrated the superiority of the system along with its robustness with other existing models. The viability of the technology for healthcare applications was also illustrated through real-time implementation through an intuitive interface. This research introduces a novel approach to diagnosing brain malignancies using multimodal imaging to set new benchmarks for accuracy and reliability.

Bharath University (IN)
Openalex Percentile: Top 87%
Brain Tumor Detection and Classification
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