A comprehensive framework for early tumor detection and quality control of MRI images using python

The early detection of brain tumors is crucial for effective treatment planning and improving patient survival rates. Magnetic Resonance Imaging (MRI) is one of the most widely used non-invasive imaging techniques for diagnosing brain tumors due to its superior soft-tissue contrast. However, the accuracy of tumor detection and classification is highly dependent on the quality of the MRI scans, which can be affected by various factors such as noise, artifacts, intensity inhomogeneity, and acquisition inconsistencies. Ensuring high-quality MRI data is, therefore, an essential prerequisite for reliable and precise tumor detection. This research project proposes a comprehensive Python-based framework that integrates deep learning models and the open-source MRQy tool to enhance early brain tumor detection while simultaneously ensuring MRI quality control. The deep learning model, based on convolutional neural networks (CNNs) and transfer learning architectures such as Reset and Efficient Net, will be trained on publicly available MRI datasets such as Bra TS (Brain Tumor Segmentation Challenge) and The Cancer Imaging Archive (TCIA). The model's performance will be evaluated using standard evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC curves, to ensure its robustness and generalizability. Additionally, MRQy will be integrated into the workflow to systematically assess and improve the quality of MRI scans by detecting common image artifacts, ensuring signal homogeneity, and standardizing intensity distributions. By incorporating advanced preprocessing techniques such as noise reduction, bias field correction, and normalization, we aim to mitigate the impact of poor-quality MRI scans on deep learning-based classification results. The expected outcomes of this research include a highly accurate and reliable deep learning model for automatic tumor detection and segmentation, an automated quality control pipeline using MRQy, and a standardized framework that can be extended to other medical imaging applications. This approach has the potential to improve diagnostic accuracy, optimize radiological workflows, and enhance clinical decision-making, ultimately leading to better patient outcomes in neuro-oncology.