부산대학교 도서관

Recent developments in deep learning have made it possible to apply it to medical image segmentation. Deep convolutional neural networks (DCNN s) are a type of deep learning model used for segmenting medical images. This approach has been found to be effective in accurately segmenting medical images, and better results in faster segmentation times than traditional methods. DCNN s use a combination of convolutional layers, pooling layers, and activation functions to detect patterns in the input images and learn a set of features from it. DCNN s have the advantage of having fewer parameters compared to other methods. This makes them easily adaptable and portable across different applications. By using the latest advances in CNN s, more accurate segmentation can be achieved in a shorter time. This makes DCNNs a powerful tool for medical image segmentation.