International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(2)

Опубликована: Апрель 15, 2025

The early detection of brain tumors is crucial for timely medical intervention and improved patient survival rates. Magnetic Resonance Imaging (MRI) the gold standard tumor diagnosis due to its superior soft-tissue contrast non-invasive nature. However, variations in MRI quality, including noise, artifacts, scanner inconsistencies, can impact diagnostic accuracy. This study aims de-velop a Python-based deep-learning model scans while integrating an automated quality control system using MRQy. MRQy, open-source tool, facilitates assessment by evaluating signal-to-noise ratios (SNR), contrast-to-noise (CNR), motion-related artifacts. deep learning will be trained on meticulously curated dataset, ensur-ing high-quality artifact-free images. By combining MRQy’s capabilities with techniques, expected en-hance accuracy reduce false-positive false-negative Furthermore, this research underscores significance standardized imaging protocols minimize variability across scanners institutions, ensuring repro-ducibility clinical AI applications. proposed approach leverages modern convolutional neural networks (CNNs) transfer incorpo-rating pre-trained architectures such as Res Net Efficient enhance fea-ture extraction. MRQy-based AI-driven classification, optimize MRI-based diagnostics, human error, improve outcomes. findings contribute ad-vancement AI-powered highlight importance

Язык: Английский