Application of Artificial Intelligence Nuclear Medicine Automated Images Based on Deep Learning in Tumor Diagnosis

In order to correctly obtain normal tissues and organs and tumor lesions, the research on multimodal medical image segmentation based on deep learning fully automatic segmentation algorithm is more meaningful. This article aims to study the application of deep learning-based artificial intelligence...

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Bibliographic Details
Published in:Journal of healthcare engineering Vol. 2022; pp. 1 - 10
Main Authors: Sun, Jian, Yuan, Xin
Format: Journal Article
Language:English
Published: England Hindawi 31.01.2022
Subjects:
Algorithms
Artificial Intelligence
Deep Learning
Humans
Image Processing, Computer-Assisted - methods
Neoplasms - diagnostic imaging
Neural Networks, Computer
Nuclear Medicine
ISSN:2040-2295, 2040-2309, 2040-2309
Online Access:Get full text
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Summary:In order to correctly obtain normal tissues and organs and tumor lesions, the research on multimodal medical image segmentation based on deep learning fully automatic segmentation algorithm is more meaningful. This article aims to study the application of deep learning-based artificial intelligence nuclear medicine automated images in tumor diagnosis. This paper studies the methods to improve the accuracy of the segmentation algorithm from the perspective of boundary recognition and shape changeable adaptive capabilities, studies the active contour model based on boundary constraints, and proposes a superpixel boundary-aware convolution network to realize the automatic CT cutting algorithm. In this way, the tumor image can be cut more accurately. The experimental results in this paper show that the improved algorithm in this paper is more robust than the traditional CT algorithm in terms of accuracy and sensitivity, an increase of about 12%, and a slight increase in the negative prediction rate of 3%. In the comparison of cutting images of malignant tumors, the cutting effect of the algorithm in this paper is about 34% higher than that of the traditional algorithm.
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Academic Editor: Bhagyaveni M.A
ISSN:2040-2295
2040-2309
2040-2309
DOI:10.1155/2022/7247549