AI-DRIVEN Novel Approach for Liver Cancer Screening and Prediction Using Cascaded Fully Convolutional Neural Network

被引：0

作者：

Shukla, Piyush Kumar ^{[1
]}

Zakariah, Mohammed ^{[2
]}

Hatamleh, Wesam Atef ^{[3
]}

Tarazi, Hussam ^{[4
]}

Tiwari, Basant ^{[5
]}

机构：

[1] Computer Science and Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal,462033, India

[2] College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh,11543, Saudi Arabia

[3] Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh,11543, Saudi Arabia

[4] Department of Computer Science and Informatics, School of Engineering and Computer Science, Oakland University, Rochester Hills,MI, United States

[5] Department of Information Technology, Hawassa University, Institute of Technology, Hawassa, Ethiopia

来源：

Journal of Healthcare Engineering | 2022年 / 2022卷

关键词：

Tumors - Convolutional neural networks - Computer aided diagnosis - Convolution - Magnetic resonance imaging - Medical computing - Computer aided analysis - Diseases;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In experimental analysis and computer-aided design sustain scheme, segmentation of cell liver and hepatic lesions by an automated method is a significant step for studying the biomarkers characteristics in experimental analysis and computer-aided design sustain scheme. Patient to patient, the change in lesion type is dependent on the size, imaging equipment (such as the setting dissimilarity approach), and timing of the lesion, all of which are different. With practical approaches, it is difficult to determine the stages of liver cancer based on the segmentation of lesion patterns. Based on the training accuracy rate, the present algorithm confronts a number of obstacles in some domains. The suggested work proposes a system for automatically detecting liver tumours and lesions in magnetic resonance imaging of the abdomen pictures by using 3D affine invariant and shape parameterization approaches, as well as the results of this study. This point-to-point parameterization addresses the frequent issues associated with concave surfaces by establishing a standard model level for the organ's surface throughout the modelling process. Initially, the geodesic active contour analysis approach is used to separate the liver area from the rest of the body. The proposal is as follows: It is possible to minimise the error rate during the training operations, which are carried out using Cascaded Fully Convolutional Neural Networks (CFCNs) using the input of the segmented tumour area. Liver segmentation may help to reduce the error rate during the training procedures. The stage analysis of the data sets, which are comprised of training and testing pictures, is used to get the findings and validate their validity. The accuracy attained by the Cascaded Fully Convolutional Neural Network (CFCN) for the liver tumour analysis is 94.21 percent, with a calculation time of less than 90 seconds per volume for the liver tumour analysis. The results of the trials show that the total accuracy rate of the training and testing procedure is 93.85 percent in the various volumes of 3DIRCAD datasets tested. © 2022 Piyush Kumar Shukla et al.

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