BISFT- two-dimensional breakdown clinical image seperation and fusion technique using CNN

G. Pradeepkumar, S. Kavitha
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引用次数: 0

Abstract

To provide the best possible performance in precisely segmenting clinical images, several approaches are used. Convolutional neural networks are one method used in it to extract its features, which combine several models with several additional methods. It also improves the efficiency of generalisation between categorised and uncategorized image categories. The method proposed combines multi-style image fusion with two-dimensional fracture image representation. The photographs on this page have been updated with a variety of images to improve concentration sharing and achieve the desired visual look. The border detection algorithm is then used to extract the exact border of the image from the contrast extended images. It will then be divided into basic and comprehensive layers. The fused image was then created using augmented end layers.
BISFT--利用 CNN 的二维分解临床图像分离与融合技术
为了提供精确分割临床图像的最佳性能,使用了多种方法。卷积神经网络是其中一种提取特征的方法,它结合了多种模型和多种附加方法。它还提高了已分类和未分类图像类别之间的泛化效率。所提出的方法将多风格图像融合与二维断裂图像表示相结合。本页面上的照片已更新为多种图像,以提高集中共享度,实现理想的视觉效果。然后使用边界检测算法从对比度扩展图像中提取图像的准确边界。然后将其分为基本层和综合层。然后使用增强端层创建融合图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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