Efficient Brain Tumor Region Segmentation Using Watershed Algorithm and Deep Learning

Identifying the exact area of a brain tumor is very important for both the diagnosis and the treatment of the disease. The manual method is so boring that it takes a lot of time and is prone to mistakes, so automated solutions are needed. This study combines the Watershed algorithm and Convolutional Neural Networks (CNN) to segment a tumor efficiently. The Watershed algorithm helps in differentiating the tumor edges, while CNN handles region allocation and verification. The hybrid method can handle issues such as noise in MRI images and the variability of tumors, thus achieving higher accuracy of segmentation with lower computational complexity. As a result of this technique, the speed of diagnosis is facilitated, which improves clinical decision-making and thus makes treatment planning and patient outcomes better. Furthermore, the proposed method can also assist in assessing tumor size and stage, which are critical for personalized treatment strategies and prognosis determination.