FF-UNet: Feature fusion based deep learning-powered enhanced framework for accurate brain tumor segmentation in MRI images

Medical imaging technology plays a crucial role in various medical sectors, aiding doctors in diagnosing patients. With brain tumors becoming a significant health concern due to their high morbidity and mortality rates, accurate and efficient tumor segmentation is essential. Manual segmentation methods are prone to errors and timeconsuming. In this study, we investigate the potential of deep learning-based brain tumor MRI image segmentation techniques. We propose an enhanced approach called FF-UNet, which leverages feature fusion and combines the power of UNet and CNN models to improve segmentation accuracy. Preprocessing techniques are employed to enhance tumor visibility, followed by the utilization of a customized layered UNet model for segmentation. To mitigate overfitting, dropout layers are introduced after each convolution block stack. Additionally, a CNN process leverages the context of brain tumor MRI images to further enhance the model's segmentation performance. Experimental results demonstrate that our proposed framework outperforms state-ofthe-art models in differentiating brain tissue. Across all datasets, our method achieves above 98% accuracy, with precision and Jaccard coefficient both exceeding 90%. Evaluation metrics such as the Jaccard index, sensitivity, and specificity validate the robust performance of our approach. The FF-UNet model holds great potential as a viable diagnostic tool, enabling radiologists to accurately segment brain tumor images and improve patient care.