The biggest challenge faced in the process of brain tumor division both here and abroad is how to ensure the accuracy of brain tumor images in the process of segmentation. The algorithm for image segmentation, such as the threshold segmentation and the region segmentation which are based on the traditional segmentation method , are more traditional in dealing with brain tumor segmentation. However, FCM algorithm is time-consuming and various defects make it difficult to guarantee the efficiency and quality when processing brain tumor images.
- According to the shortcomings of the above algorithm in brain tumor image detection, in this project, a brain tumor image segmentation
- algorithm based on improved U-Net network is proposed, which realizes the training length, strong robustness, and accurately and
- completely divides the brain tumor image. By applying image recognition and deep learning techniques, U-Net network's brain tumor
- image segmentation algorithm can help doctors locate diseases, analyze conditions, assist in diagnosis, and improve their productivity withou
- t artificial intervention, which has significance clinical guiding for diagnosis and treatment.
As the healthcare system in the U.S. moves toward a value-based care model that emphasizes quality over quantity, preventative medicine is becoming more important. The new movement of active health surveillance through services such as whole-body MRIs is also looks to play a crucial role.
One of Prenuvo's goals is to make active health surveillance affordable and accessible for everyone. The current whole-body MRI service costs about $2,499 and takes an hour, but Prenuvo wants to make changes.
"Our goal is to make this service affordable for everybody by charging $300 per scan. We also want each scan to take 15 minutes," says Attariwala. "Everyone should have a whole-body MRI scan once a year."