基于视觉和文本特征提取技术的医疗领域放射学图像骨骼图像视觉问答系统

Q1 Decision Sciences
Jinesh Melvin Y.I., Mukesh Shrimali, Sushopti Gawade
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引用次数: 0

摘要

医学影像查询响应系统是医学领域最具挑战性的概念之一。它需要大量的努力来组织和理解人体的各种表现形式。此外,该系统需要由医疗保健行业的用户进行验证。借助各种图像,包括核磁共振扫描、CT扫描、超声波、x射线、PET-CT扫描等,有可能识别人类健康问题。预计将鼓励患者参与和支持临床决策。由于使用了许多与医学图像和问题不充分匹配的特征,从技术上讲,医疗保健领域的VQA系统比普通领域的VQA系统更复杂。这些挑战是由用于视觉和文本方面的数据集、方法和模型引起的。这有时会使临床援助更难提供相关的答案。该系统将分析当前模型并诊断问题,以改进针对最新数据集的医学可视化问答系统。与该模型比较的模型有卷积神经网络(CNN)、深度信念网络(DBN)、循环神经网络(RNN)、长短期记忆网络(LSTM)和双向长短期记忆(BiLSTM)。为了评估每个模型的有效性,应采用以下措施:分类精度、f -分类、f -测度、c -假阴性率(FNR)、c -阳性预测值、C-Precision、C-Recall、C-Sensitivity和c -真阳性率(CTPR)。目的是提高任何数据集的性能,并对视觉和文本特征进行准确性和度量,以获得给定问题的正确答案。所提出的系统有助于识别现有模型的理想程度,并使用B12 FASTER递归神经网络(RNN)和Kai-Bi-LSTM生成新模型。通过问题和适当的答案,建议的模型将有助于提取导入的图像和文本的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual Question Answer System for Skeletal Image Using Radiology Images in the Healthcare Domain Based on Visual and Textual Feature Extraction Techniques

The Medical Imaging Query Response System is among the most challenging concepts in the medical field. It requires a significant amount of effort to organize and comprehend the various representations of the human body. Additionally, the system needs to be verified by users in the healthcare industry. With the aid of various images, including MRI scans, CT scans, ultrasounds, X-rays, PET-CT scans, and more, it may be possible to identify human health issues. It is anticipated to encourage patient participation and support clinical decision-making. As a result of the use of a number of characteristics that are inadequately matched to medical images and questions, technically, the VQA system in the healthcare domain is more complicated than in the common domain. The challenges were caused by the datasets, approaches, and models used for both visual and textual aspects. This can sometimes make it harder for clinical assistance to provide relevant answers. The proposed system will analyze current models and diagnose the problem in order to improve the medical visual question-answering system for recent datasets. The models that were compared to the model were convolutional neural networks (CNN), deep belief networks (DBN), recurrent neural networks (RNN), long short-term memory networks (LSTM), and bidirectional long short-term memory (BiLSTM). To assess the effectiveness of each model, the following measures should be used: Classification Accuracy, F-Classification, F-Measure, C-False Negative Rate (FNR), C-Positive Predictive Value, C-Precision, C-Recall, C-Sensitivity, and C-True Positive Rate (CTPR) With the objective of improving the performance of any dataset with accuracy and measures for both visual and textual features to get the right answers for given questions, the proposed system helps to recognize how ideal the existing models are and generates new models using the B12 FASTER Recurrent Neural Network (RNN) and Kai-Bi-LSTM. With questions and appropriate answers, the suggested model will assist in extracting the features of imported images and text.

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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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