Multi-Modal Image Fusion for Early Disease Diagnosis: AI in Medical Imaging
DOI:
https://doi.org/10.54228/mjaret0624010Keywords:
Multi-Modal Image Fusion; Artificial Intelligence; Medical Imaging; Early Disease Diagnosis; Deep Learning; Self-Supervised Learning; Feature Synthesis; Brain Tumor Detection; Lung Cancer DiagnosisAbstract
This study focuses on a novel multi-modal image fusion algorithm with artificial intelligence in medical imaging for early-stage disease diagnosis. We introduced Deep Multi-Cascade Fusion (DMC-Fusion) algorithm, which fuses classifier-based features from MRI, CT and PET with self-supervised learning techniques. By leveraging a unique dataset of 10,000 multi-modal medical images of five different types of diseases, including brain tumors and lung cancer, the proposed method outperformed existing single-modality imaging systems by improving accuracy in the detection of early-stage diseases to 25%. In the proposed AI-driven fusion framework, the fusion algorithm achieved a sensitivity of 92%and specificity of 95% in identifying malignancies in very early stages with great accuracy. This research led to a robust diagnostic tool for improving disease detection and implementation of effective treatment, thus improving patient outcomes with early interventions.
Downloads
Downloads
Published
Issue
Section
License
License Terms for MJARET
Creative Commons Attributio 4.0 International (CC BY) License:
This license allows for the following:
-
Sharing — Copy and redistribute the material in any medium or format.
-
Adaptation — Remix, transform, and build upon the material.
The license is subject to the following terms:
-
Attribution:
- You must give appropriate credit, provide a link to the license, and indicate if changes were made.
- Attribution should include the citation of the article, the author's name, and a link to the original work published in MJARET.
- This must be done in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NonCommercial:
- The material cannot be used for commercial purposes.
- Any use of the work intended to provide a commercial advantage or monetary compensation is considered outside the scope of this license.
-
No Additional Restrictions:
- You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
General Provisions:
- Understandability: This license can be revoked if you do not comply with its terms and conditions.
- Public Domain: Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- Other Rights:
- The license does not cover rights such as publicity, privacy, or moral rights that may affect your ability to use the material as contemplated by the license.
- Such rights might need to be considered and respected separately.
Disclaimer:
- MJARET does not provide any warranties with the work. The work is provided "as is" without any representations or warranties, express or implied. MJARET will not be liable for any damages resulting from the use of the work.
How to Cite:
- Proper attribution for use of the licensed work should follow the standard citation format provided by MJARET, which should include the author(s), the title of the work, MJARET, and the DOI link.
