Integrating Deep Learning and MRQy: A Comprehensive Framework for Early Detection and Quality Control of Brain Tumors in MRI Images using Python

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