Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm

The purpose of this study is to explore the application value of artificial intelligence algorithm in multimodal MRI image diagnosis of cervical cancer. Based on the traditional convolutional neural network (CNN), an artificial intelligence 3D-CNN algorithm is designed according to the characteristi...

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Autores principales:	Bin Wang, Yuanyuan Zhang, Chunyan Wu, Fen Wang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/24590e3ebf9f4cf68c41e1097a68b421
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spelling	oai:doaj.org-article:24590e3ebf9f4cf68c41e1097a68b4212021-11-22T01:11:16ZMultimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm1555-431710.1155/2021/1673490https://doaj.org/article/24590e3ebf9f4cf68c41e1097a68b4212021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1673490https://doaj.org/toc/1555-4317The purpose of this study is to explore the application value of artificial intelligence algorithm in multimodal MRI image diagnosis of cervical cancer. Based on the traditional convolutional neural network (CNN), an artificial intelligence 3D-CNN algorithm is designed according to the characteristics of cervical cancer. 70 patients with cervical cancer were selected as the experimental group, and 10 healthy people were selected as the reference group. The 3D-CNN algorithm was applied to the diagnosis of clinical cervical cancer multimodal MRI images. The value of the algorithm was comprehensively evaluated by the image quality and diagnostic accuracy. The results showed that compared with the traditional CNN algorithm, the convergence rate of the loss curve of the artificial intelligence 3D-CNN algorithm was accelerated, and the segmentation accuracy of whole-area tumors (WT), core tumor areas (CT), and enhanced tumor areas (ET) was significantly improved. In addition, the clarity of the multimodal MRI image and the recognition performance of the lesion were significantly improved. Under the artificial intelligence 3D-CNN algorithm, the Dice values of WT, ET, and CT regions were 0.78, 0.71, and 0.64, respectively. The sensitivity values were 0.92, 0.91, and 0.88, respectively. The specificity values were 0.93, 0.92, and 0.9 l, respectively. The Hausdorff (Haus) distances were 0.93, 0.92, and 0.90, respectively. The data of various indicators were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of the artificial intelligence 3D-CNN algorithm was 93.11 ± 4.65%, which was also significantly higher than that of the traditional CNN algorithm (82.45 ± 7.54%) (P < 0.05). In summary, the recognition and segmentation ability of multimodal MRI images based on artificial intelligence 3D-CNN algorithm for cervical cancer lesions were significantly improved, which can significantly enhance the clinical diagnosis rate of cervical cancer.Bin WangYuanyuan ZhangChunyan WuFen WangHindawi-WileyarticleMedical technologyR855-855.5ENContrast Media & Molecular Imaging, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medical technology R855-855.5
spellingShingle	Medical technology R855-855.5 Bin Wang Yuanyuan Zhang Chunyan Wu Fen Wang Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm
description	The purpose of this study is to explore the application value of artificial intelligence algorithm in multimodal MRI image diagnosis of cervical cancer. Based on the traditional convolutional neural network (CNN), an artificial intelligence 3D-CNN algorithm is designed according to the characteristics of cervical cancer. 70 patients with cervical cancer were selected as the experimental group, and 10 healthy people were selected as the reference group. The 3D-CNN algorithm was applied to the diagnosis of clinical cervical cancer multimodal MRI images. The value of the algorithm was comprehensively evaluated by the image quality and diagnostic accuracy. The results showed that compared with the traditional CNN algorithm, the convergence rate of the loss curve of the artificial intelligence 3D-CNN algorithm was accelerated, and the segmentation accuracy of whole-area tumors (WT), core tumor areas (CT), and enhanced tumor areas (ET) was significantly improved. In addition, the clarity of the multimodal MRI image and the recognition performance of the lesion were significantly improved. Under the artificial intelligence 3D-CNN algorithm, the Dice values of WT, ET, and CT regions were 0.78, 0.71, and 0.64, respectively. The sensitivity values were 0.92, 0.91, and 0.88, respectively. The specificity values were 0.93, 0.92, and 0.9 l, respectively. The Hausdorff (Haus) distances were 0.93, 0.92, and 0.90, respectively. The data of various indicators were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of the artificial intelligence 3D-CNN algorithm was 93.11 ± 4.65%, which was also significantly higher than that of the traditional CNN algorithm (82.45 ± 7.54%) (P < 0.05). In summary, the recognition and segmentation ability of multimodal MRI images based on artificial intelligence 3D-CNN algorithm for cervical cancer lesions were significantly improved, which can significantly enhance the clinical diagnosis rate of cervical cancer.
format	article
author	Bin Wang Yuanyuan Zhang Chunyan Wu Fen Wang
author_facet	Bin Wang Yuanyuan Zhang Chunyan Wu Fen Wang
author_sort	Bin Wang
title	Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm
title_short	Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm
title_full	Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm
title_fullStr	Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm
title_full_unstemmed	Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm
title_sort	multimodal mri analysis of cervical cancer on the basis of artificial intelligence algorithm
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/24590e3ebf9f4cf68c41e1097a68b421
work_keys_str_mv	AT binwang multimodalmrianalysisofcervicalcanceronthebasisofartificialintelligencealgorithm AT yuanyuanzhang multimodalmrianalysisofcervicalcanceronthebasisofartificialintelligencealgorithm AT chunyanwu multimodalmrianalysisofcervicalcanceronthebasisofartificialintelligencealgorithm AT fenwang multimodalmrianalysisofcervicalcanceronthebasisofartificialintelligencealgorithm
_version_	1718418268196175872

Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm

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