(untitled) (Journal of Pharmaceutical Negative Results, 2024)
Brain tumors are a global health problem and must be diagnosed accurately and in a timely manner in order to receive good treatment. Magnetic resonance imaging (MRI) is the first recommendation, but MRI scans remain difficult to interpret for brain segmentation. This paper details the design and application of a customized convolutional neural network (CNN) architecture that is integrated with the FastAI library for brain tumor segmentation. The system includes modules for data loading, preprocessing, feature extraction, model training and output generation. Through rigorous analysis and measurement of different data, including performance measures such as precision, recall, and Dice coefficient, the system demonstrates reliability, efficiency and performance over time. Implementation of the system involves the deployment of a user-friendly interface for clinical use. The project also highlights the importance of maintaining the system to ensure long-term reliability and efficiency. Future work includes optimization, integration with new technologies, and improvement of segmentation techniques to improve diagnostic accuracy and patient impact on treatment.