人工智能
分割
计算机科学
卷积神经网络
深度学习
图像分割
工件(错误)
图像复原
噪音(视频)
领域(数学)
模式识别(心理学)
尺度空间分割
透视图(图形)
计算机视觉
图像(数学)
图像处理
数学
纯数学
作者
Hepzibah A. Christinal,R. Hephzibah,G. Kowsalya,Ram Prateek Jayanthi,D. Abraham Chandy
出处
期刊:Current Medical Imaging Reviews
[Bentham Science]
日期:2023-07-01
卷期号:19 (8)
标识
DOI:10.2174/1573405618666220407112825
摘要
This paper comprehensively reviews two major image processing tasks, such as restoration and segmentation in the medical field, from a deep learning perspective. These processes are essential because restoration removes noise and segmentation extracts the specific region of interest of an image, both of which are necessary for accurate diagnosis and therapy. This paper mainly focuses on deep learning techniques. It plays a prominent role over other conventional techniques in handling a large number of datasets in the medical field and provides accurate results. This paper reviewed the application of different convolutional neural network architectures in the restoration and segmentation processes. Based on the results in the case of image restoration, TLR-CNN and Stat-CNN are promising in achieving better PSNR, noise suppression, artifact suppression and improving the overall image quality. For the segmentation process, LCP net achieves the Dice score of 98.12% and sensitivity of 98.95% in the cell contour segmentation; the 3D FCNN model is found to be the best method for the segmentation of brain tumors. This review shows that deep learning methodologies can be a better alternative for medical image restoration and segmentation tasks, as data size is an important concern today.
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