To understand how Artificial Intelligence (AI) can aid in early detection of brain tumors using MRI scans.
To explore image segmentation techniques for accurately identifying tumor-affected regions in MRI images.
To design and implement a machine learning or deep learning model for classifying and segmenting brain tumors.
To evaluate the model's accuracy, sensitivity, and specificity in real-world diagnosis scenarios.
To assess the potential of AI-assisted diagnostics in improving clinical outcomes and reducing radiologist workload.
Conduct a literature review on brain tumor types, MRI scan analysis, and AI-based diagnostic systems.
Collect and preprocess publicly available MRI datasets (e.g., BRATS) for training and testing.
Apply image processing techniques such as noise removal, normalization, and contrast enhancement.
Implement segmentation algorithms (e.g., CNNs, U-Net, K-means clustering) to extract tumor regions.
Train and validate the AI model using appropriate metrics like Dice coefficient, accuracy, precision, and recall.
Develop a basic UI or visualization tool to show segmented tumor areas on MRI slices.
Prepare a comprehensive project report detailing the methodology, model performance, limitations, and future enhancements.
