The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)an...The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.展开更多
This study aims to apply ResNet-18 convolutional neural network(CNN)and XGBoost to preoperative computed tomography(CT)images and clinical data for distinguishing Xp11.2 translocation renal cell carcinoma(Xp11.2 tRCC)...This study aims to apply ResNet-18 convolutional neural network(CNN)and XGBoost to preoperative computed tomography(CT)images and clinical data for distinguishing Xp11.2 translocation renal cell carcinoma(Xp11.2 tRCC)from common subtypes of renal cell carcinoma(RCC)in order to provide patients with individualized treatment plans.Data from45 patients with Xp11.2 tRCC fromJanuary 2007 to December 2021 are collected.Clear cell RCC(ccRCC),papillary RCC(pRCC),or chromophobe RCC(chRCC)can be detected from each patient.CT images are acquired in the following three phases:unenhanced,corticomedullary,and nephrographic.A unified framework is proposed for the classification of renal masses.In this framework,ResNet-18 CNN is employed to classify renal cancers with CT images,while XGBoost is adopted with clinical data.Experiments demonstrate that,if applying ResNet-18 CNN or XGBoost singly,the latter outperforms the former,while the framework integrating both technologies performs similarly or better than urologists.Especially,the possibility of misclassifying Xp11.2 tRCC,pRCC,and chRCC as ccRCC by the proposed framework is much lower than urologists.展开更多
Even though much advancements have been achieved with regards to the recognition of handwritten characters,researchers still face difficulties with the handwritten character recognition problem,especially with the adv...Even though much advancements have been achieved with regards to the recognition of handwritten characters,researchers still face difficulties with the handwritten character recognition problem,especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset(EMNIST).The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class variability.Inter-class similarity exists because of the similarity between the shapes of certain characters in the dataset.The presence of intra-class variability is mainly due to different shapes written by different writers for the same character.In this research,we have optimized a deep residual network to achieve higher accuracy vs.the published state-of-the-art results.This approach is mainly based on the prebuilt deep residual network model ResNet18,whose architecture has been enhanced by using the optimal number of residual blocks and the optimal size of the receptive field of the first convolutional filter,the replacement of the first max-pooling filter by an average pooling filter,and the addition of a drop-out layer before the fully connected layer.A distinctive modification has been introduced by replacing the final addition layer with a depth concatenation layer,which resulted in a novel deep architecture having higher accuracy vs.the pure residual architecture.Moreover,the dataset images’sizes have been adjusted to optimize their visibility in the network.Finally,by tuning the training hyperparameters and using rotation and shear augmentations,the proposed model outperformed the state-of-the-art models by achieving average accuracies of 95.91%and 90.90%for the Letters and Balanced dataset sections,respectively.Furthermore,the average accuracies were improved to 95.9%and 91.06%for the Letters and Balanced sections,respectively,by using a group of 5 instances of the trained models and averaging the output class probabilities.展开更多
文摘The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.
基金supported by Beijing Ronghe Medical Development Foundation。
文摘This study aims to apply ResNet-18 convolutional neural network(CNN)and XGBoost to preoperative computed tomography(CT)images and clinical data for distinguishing Xp11.2 translocation renal cell carcinoma(Xp11.2 tRCC)from common subtypes of renal cell carcinoma(RCC)in order to provide patients with individualized treatment plans.Data from45 patients with Xp11.2 tRCC fromJanuary 2007 to December 2021 are collected.Clear cell RCC(ccRCC),papillary RCC(pRCC),or chromophobe RCC(chRCC)can be detected from each patient.CT images are acquired in the following three phases:unenhanced,corticomedullary,and nephrographic.A unified framework is proposed for the classification of renal masses.In this framework,ResNet-18 CNN is employed to classify renal cancers with CT images,while XGBoost is adopted with clinical data.Experiments demonstrate that,if applying ResNet-18 CNN or XGBoost singly,the latter outperforms the former,while the framework integrating both technologies performs similarly or better than urologists.Especially,the possibility of misclassifying Xp11.2 tRCC,pRCC,and chRCC as ccRCC by the proposed framework is much lower than urologists.
文摘针对果实分拣中存在识别精度低、耗时长等问题,设计实现了一种基于深度学习的智能水果分拣系统.首先,该系统采用残差网络(Residual network,ResNet)模型,通过引入动态残差门控机制优化梯度传播有效解决了深层网络训练中的梯度消失和爆炸问题,使得网络能够通过跳跃连接学习到更有效的特征表示;其次,对ResNet-18模型进行了轻量化设计,利用交叉熵损失函数(CrossEntropy loss,CELoss)和Adam优化器(Adaptive moment estimation,Adam)来进行模型的训练;最后,对数据集peach-split进行实验分析,结果表明构建的智能分拣系统对提高水果分拣精度研究具有一定的实用价值.
文摘Even though much advancements have been achieved with regards to the recognition of handwritten characters,researchers still face difficulties with the handwritten character recognition problem,especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset(EMNIST).The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class variability.Inter-class similarity exists because of the similarity between the shapes of certain characters in the dataset.The presence of intra-class variability is mainly due to different shapes written by different writers for the same character.In this research,we have optimized a deep residual network to achieve higher accuracy vs.the published state-of-the-art results.This approach is mainly based on the prebuilt deep residual network model ResNet18,whose architecture has been enhanced by using the optimal number of residual blocks and the optimal size of the receptive field of the first convolutional filter,the replacement of the first max-pooling filter by an average pooling filter,and the addition of a drop-out layer before the fully connected layer.A distinctive modification has been introduced by replacing the final addition layer with a depth concatenation layer,which resulted in a novel deep architecture having higher accuracy vs.the pure residual architecture.Moreover,the dataset images’sizes have been adjusted to optimize their visibility in the network.Finally,by tuning the training hyperparameters and using rotation and shear augmentations,the proposed model outperformed the state-of-the-art models by achieving average accuracies of 95.91%and 90.90%for the Letters and Balanced dataset sections,respectively.Furthermore,the average accuracies were improved to 95.9%and 91.06%for the Letters and Balanced sections,respectively,by using a group of 5 instances of the trained models and averaging the output class probabilities.
