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Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering.
- Source :
-
BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Apr 30; Vol. 24 (1), pp. 113. Date of Electronic Publication: 2024 Apr 30. - Publication Year :
- 2024
-
Abstract
- Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).<br /> (© 2024. The Author(s).)
Details
- Language :
- English
- ISSN :
- 1472-6947
- Volume :
- 24
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- BMC medical informatics and decision making
- Publication Type :
- Academic Journal
- Accession number :
- 38689289
- Full Text :
- https://doi.org/10.1186/s12911-024-02519-x