Enhanced Brain Tumor Segmentation using Convolutional Neural Network

Brain tumor segmentation is an important process in medical imaging, and that is to properly delineate tumor areas from brain MRI images. Early and accurate detection of tumors is important for better patient outcomes as it contributes to the diagnosis, planning of treatment, and assessment of prognosis. Traditional segmentation algorithms rely significantly on radiologists' manual tracing, which is labor-intensive, subjective, and suffers from inter-observer variability. Deep learning techniques, particularly convolutional neural networks (CNNs) and advanced architectures like U-Net, have also been shown to have great performance in automatic tumor segmentation and improved tumor segmentation accuracy. Here, we develop a deep learning-based brain tumor segmentation model using MRI scans for effective segmentation and classification of tumor areas. The model adopts an encoder-decoder framework with attention mechanisms for boosting feature extraction and segmentation accuracy. With such datasets as the Brain Tumor Segmentation (BraTS) challenge dataset, we train the model for different types of tumors like gliomas, meningiomas, and metastases. Transfer learning and fine-tuning techniques are also employed to enhance generalization so that the model performs optimally on different datasets and real clinical settings. To evaluate performance, we use common metrics like Dice Similarity Coefficient (DSC), Intersection over Union (IoU), precision, and recall.