Automated Medical Image Analysis for Brain Tumour Detection Using DL

Abstract

Brain Tumors are one of the most critical health issues, requiring timely detection for effective treatment. Traditional detection methods, such as MRI images, rely on manual interpretation by radiologists, which can be time-consuming and prone to errors. This project proposes a deep learningbased system for automated brain tumor detection using medical imaging data. Convolutional Neural Networks (CNNs) are utilized to analyze MRI images and identify tumor regions accurately. Preprocessing techniques such as normalization, noise reduction, and image augmentation improve model performance. The system classifies tumors into benign and malignant categories. Feature extraction and segmentation allow precise localization of tumors. The deep learning model learns complex patterns that may be difficult for human interpretation. Real-time analysis reduces diagnostic delays. Integration with hospital management systems streamlines patient records. Ensemble learning improves classification accuracy. Performance evaluation uses metrics like accuracy, precision, recall, and F1-score. Adaptive learning allows the model to improve with new datasets. Cloud deployment enables scalability for large hospitals. Visualization tools display tumor regions for radiologists. Automated detection reduces dependency on manual diagnosis. It minimizes human errors and improves patient care. The system contributes to faster, reliable, and intelligent brain tumor diagnosis. Overall, AI-driven detection enhances efficiency in medical imaging analysis.