Advancing Accuracy with Ai-Driven Approaches for Automated Detection and Segmentation of Brain Tumours
DOI:
https://doi.org/10.48047/CU/54/03/2192-2199Keywords:
Accuracy AI; Automated Detection; Segmentation; Brain Tumours; AI-driven Methodology.Abstract
The presence of brain tumours is a significant medical issue that calls for precise detection and diagnosis, particularly in the field of magnetic resonance imaging (MRI). The existing methods, which are relying on traditional image processing and conventional machine learning, face difficulties in accurately identifying the sites of tumours within complicated MRI scans. These images are frequently impacted by noise and have uneven picture quality
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References
Zubair Rahman, A. M. J., Gupta, M., Aarathi, S., Mahesh, T. R., Vinoth Kumar, V., Yogesh Kumaran, S., & Guluwadi, S. (2024). Advanced AI-driven approach for enhanced brain tumour detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering. BMC Medical Informatics and Decision Making, 24(1), 113. 2. Khalighi, S., Reddy, K., Midya, A., Pandav, K. B., Madabhushi, A., & Abedalthagafi, M. (2024). Artificial intelligence in neuro-oncology: advances and challenges in brain tumour diagnosis, prognosis, and precision treatment. NPJ Precision Oncology, 8(1), 80.
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