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Efficient Prediction of Seasonal Infectious Diseases Using Hybrid Machine Learning Algorithms with Feature Selection Techniques 基于混合机器学习算法和特征选择技术的季节性传染病有效预测
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-04-20 DOI: 10.1142/s0218213023500446
K. Indhumathi, K. Kumar
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引用次数: 0
Pinakas: A Methodology for Deep Analysis of Tables in Technical Documents Pinakas:技术文件中表格的深度分析方法
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-04-20 DOI: 10.1142/s0218213023500422
M. S. Alexiou, N. Bourbakis
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引用次数: 1
Transfer Learning-based Drowsiness Detection System for Driver Assistance and Classification of Traffic Signs Employing a Deep Convolutional Neural Network 基于深度卷积神经网络的驾驶员辅助和交通标志分类睡意检测系统
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-04-04 DOI: 10.1142/s0218213023500355
A. Deshpande, M. Subashini
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引用次数: 0
A Hybrid CNN Model for Deep Feature Extraction for Diabetic Retinopathy Detection and Gradation 一种用于糖尿病视网膜病变检测与分级的混合CNN深度特征提取模型
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-03-22 DOI: 10.1142/s0218213023500367
N. Mukherjee, S. Sengupta
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引用次数: 0
Deep Ensemble Classification with Self Improved Optimization for Attack Detection Towards Secured Virtualization in Cloud 基于自改进优化的深度集成分类云安全虚拟化攻击检测
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-03-22 DOI: 10.1142/s0218213023500380
B. Gupta, N. Mishra
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引用次数: 0
Enhancing Dynamic Multi-objective Optimization Using Opposition-based Learning and Simulated Annealing 基于对立学习和模拟退火的动态多目标优化
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-03-22 DOI: 10.1142/s0218213023500379
K. Ilyas, I. Younas
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引用次数: 0
Predictive Learning Methods to Price European Options Using Ensemble Model and Multi-asset Data 基于集合模型和多资产数据的欧洲期权定价预测学习方法
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-03-07 DOI: 10.1142/s0218213023500343
K. Shubham, V. Tiwari, K. Patel
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引用次数: 0
An Adaptive Learning Environment for Programming Based on Fuzzy Logic and Machine Learning 基于模糊逻辑和机器学习的编程自适应学习环境
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-03-07 DOI: 10.1142/s0218213023600114
K. Chrysafiadi, M. Virvou, G. Tsihrintzis, I. Hatzilygeroudis
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引用次数: 0
Editorial: Special Issue on Data Mining, Machine Learning and Decision Support Systems in Health Care 社论:医疗保健中的数据挖掘、机器学习和决策支持系统特刊
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-03-01 DOI: 10.1142/s0218213023020013
A. Valls, T. Alsinet, A. Moreno
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引用次数: 0
Ultrasound Image Segmentation and Classification of Benign and Malignant Thyroid Nodules on the Basis of Deep Learning 基于深度学习的甲状腺良恶性结节超声图像分割与分类
IF 1.1 4区 计算机科学 Q3 Computer Science Pub Date : 2023-03-01 DOI: 10.1142/s0218213023400031
Min Yang, Austin Lin Yee, Jiafeng Yu
This study aimed to investigate the effect of an image denoising algorithm based on weighted low-rank matrix restoration on thyroid nodule ultrasound images. A total of 1000 original ultrasound image data sets of thyroid nodules were selected as the study samples. The nodule segmentation data set of thyroid ultrasound region of interest (ROI) images was drawn and acquired. By introducing multiscale features and an attention mechanism to optimize the U-Net model, an ultrasound image segmentation model (F-U-Net) was constructed. The performance of the traditional U network model and full convolutional neural network model (FCN) was analyzed and compared by simulation experiments. The results showed that the dice coefficient, accuracy, and recall of the improved loss function in this study were significantly higher than those of the traditional cross entropy loss function and dice coefficient loss function, and the differences were statistically significant (P < 0.05). The Dice coefficient, accuracy, and recall of the F-U-net model were significantly higher than those of the traditional FCN model and U-net model (P < 0.05). The diagnostic sensitivity, specificity, accuracy, and positive predictive value of the F-U-net model for benign and malignant thyroid nodules were significantly higher than those of the FCN model and U-net model (P < 0.05). In summary, the proposed F-U network can effectively process the ultrasound images of thyroid nodules, improve the image quality, and help to improve the diagnostic effect of benign and malignant thyroid nodules. It provides a data reference for segmentation and reconstruction of benign and malignant ultrasound images of thyroid nodules.
本研究旨在探讨一种基于加权低秩矩阵恢复的图像去噪算法对甲状腺结节超声图像的影响。选取1000个甲状腺结节的原始超声图像数据集作为研究样本。绘制并获取甲状腺超声感兴趣区域(ROI)图像的结节分割数据集。通过引入多尺度特征和注意机制对U-Net模型进行优化,构建了超声图像分割模型(F-U-Net)。通过仿真实验对传统U网络模型和全卷积神经网络模型(FCN)的性能进行了分析和比较。结果表明,本研究改进的损失函数的骰子系数、准确率、召回率均显著高于传统的交叉熵损失函数和骰子系数损失函数,差异均有统计学意义(P < 0.05)。F-U-net模型的Dice系数、准确率和召回率均显著高于传统FCN模型和U-net模型(P < 0.05)。F-U-net模型对甲状腺良恶性结节的诊断敏感性、特异性、准确性及阳性预测值均显著高于FCN模型和U-net模型(P < 0.05)。综上所述,本文提出的F-U网络能够有效处理甲状腺结节的超声图像,提高图像质量,有助于提高甲状腺结节良恶性的诊断效果。为甲状腺结节良恶性超声图像的分割与重建提供数据参考。
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引用次数: 0
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International Journal on Artificial Intelligence Tools
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