肺炎检测的增强特征选择算法

S. Abdullah, Wafaa M. Salih Abedi, R. Hadi
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引用次数: 2

摘要

肺炎是一种可以通过x射线图像检测到的肺部疾病。胸部x线图像分析是医学图像分析和计算机辅助放射学中一个活跃的研究领域。本研究旨在通过提供一种识别和分类疾病的技术来提高放射科医生工作的准确性和效率。应该更多地关注应用机器学习方法来开发一种鲁棒的胸部x线图像分类方法。检测肺炎的典型方法是通过胸部x射线图像,但分析这些图像可能很复杂,需要放射技师的专业知识。本文论证了以胸部x线图像为数据集,结合朴素贝叶斯分类器的支持向量机,以PCA和GA作为特征选择方法检测疾病的可行性。所选择的特征对于训练许多分类器是必不可少的。该系统使用91%的主成分,准确率达到92.26%。研究结果表明,使用PCA和GA进行胸片图像分类的特征选择可以达到97.44%的良好准确率。需要进一步研究探索使用其他数据挖掘模型和护理组件来提高系统的准确性和有效性。
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Enhanced feature selection algorithm for pneumonia detection
Pneumonia is a type of lung disease that can be detected using X-ray images. The analysis of chest X-ray images is an active research area in medical image analysis and computer-aided radiology. This research aims to improve the accuracy and efficiency of radiologists' work by providing a technique for identifying and categorizing diseases. More attention should be given to applying machine learning approaches to develop a robust chest X-ray image classification method. The typical method for detecting Pneumonia is through chest X-ray images but analyzing these images can be complex and requires the expertise of a radiographer. This paper demonstrates the feasibility of detecting the disease using chest X-ray images as datasets and a Support Vector Machine combined with a Naive Bayesian classifier, with PCA and GA as feature selection methods. The selected features are essential for training many classifiers. The proposed system achieved an accuracy of 92.26%, using 91% of the principal component. The study's result suggests that using PCA and GA for feature selection in chest X-ray image classification can achieve a good accuracy of 97.44%. Further research is needed to explore the use of other data mining models and care components to improve the accuracy and effectiveness of the system.
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来源期刊
CiteScore
1.90
自引率
0.00%
发文量
140
审稿时长
7 weeks
期刊介绍: *Industrial Engineering: 1 . Ergonomics 2 . Manufacturing 3 . TQM/quality engineering, reliability/maintenance engineering 4 . Production Planning 5 . Facility location, layout, design, materials handling 6 . Education, case studies 7 . Inventory, logistics, transportation, supply chain management 8 . Management 9 . Project/operations management, scheduling 10 . Information systems for production and management 11 . Innovation, knowledge management, organizational learning *Mechanical Engineering: 1 . Energy 2 . Machine Design 3 . Engineering Materials 4 . Manufacturing 5 . Mechatronics & Robotics 6 . Transportation 7 . Fluid Mechanics 8 . Optical Engineering 9 . Nanotechnology 10 . Maintenance & Safety *Computer Science: 1 . Computational Intelligence 2 . Computer Graphics 3 . Data Mining 4 . Human-Centered Computing 5 . Internet and Web Computing 6 . Mobile and Cloud computing 7 . Software Engineering 8 . Online Social Networks *Electrical and electronics engineering 1 . Sensor, automation and instrumentation technology 2 . Telecommunications 3 . Power systems 4 . Electronics 5 . Nanotechnology *Architecture: 1 . Advanced digital applications in architecture practice and computation within Generative processes of design 2 . Computer science, biology and ecology connected with structural engineering 3 . Technology and sustainability in architecture *Bioengineering: 1 . Medical Sciences 2 . Biological and Biomedical Sciences 3 . Agriculture and Life Sciences 4 . Biology and neuroscience 5 . Biological Sciences (Botany, Forestry, Cell Biology, Marine Biology, Zoology) [...]
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