Knee osteoarthritis(OA)is a common disease that impairs knee function and causes pain.Currently,studies on the detection of knee OA mainly focus on X-ray images,but X-ray images are insensitive to the changes in knee ...Knee osteoarthritis(OA)is a common disease that impairs knee function and causes pain.Currently,studies on the detection of knee OA mainly focus on X-ray images,but X-ray images are insensitive to the changes in knee OA in the early stage.Since magnetic resonance(MR)imaging can observe the early features of knee OA,the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered.Firstly,the knee MR images are preprocessed before training,including a region of interest clipping,slice selection,and data augmentation.Then the data set was divided by patient-level and the knee OA was classified by the deep transfer learning method based on the DenseNet201 model.The method divides the training process into two stages.The first stage freezes all the base layers and only trains the weights of the embedding neural networks.The second stage unfreezes part of the base layers and trains the unfrozen base layers and the weights of the embedding neural network.In this step,we design a block-by-block fine-tuning strategy for training based on the dense blocks,which improves detection accuracy.We have conducted training experiments with different depth modules,and the experimental results show that gradually adding more dense blocks in the fine-tuning can make the model obtain better detection performance than only training the embedded neural network layer.We achieve an accuracy of 0.921,a sensitivity of 0.960,a precision of 0.885,a specificity of 0.891,an F1-Score of 0.912,and an MCC of 0.836.The comparative experimental results on the OAI-ZIB dataset show that the proposed method outperforms the other detection methods with the accuracy of 92.1%.展开更多
COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over th...COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over the world.The healthcare sector of the world is facing great challenges tackling COVID cases.One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases.In this article,we propose a deep Convo-lutional Neural Network(CNN)based approach to detect COVID+(i.e.,patients with COVID-19),pneumonia and normal cases,from the chest X-ray images.COVID-19 detection from chest X-ray is suitable considering all aspects in compar-ison to Reverse Transcription Polymerase Chain Reaction(RT-PCR)and Computed Tomography(CT)scan.Several deep CNN models including VGG16,InceptionV3,DenseNet121,DenseNet201 and InceptionResNetV2 have been adopted in this pro-posed work.They have been trained individually to make particular predictions.Empirical results demonstrate that DenseNet201 provides overall better performance with accuracy,recall,F1-score and precision of 94.75%,96%,95%and 95%respec-tively.After careful comparison with results available in the literature,we have found to develop models with a higher reliability.All the studies were carried out using a publicly available chest X-ray(CXR)image data-set.展开更多
基金The authors extend their appreciation to the Jilin Provincial Natural Science Foundation for funding this research work through Project Number(20220101128JC).
文摘Knee osteoarthritis(OA)is a common disease that impairs knee function and causes pain.Currently,studies on the detection of knee OA mainly focus on X-ray images,but X-ray images are insensitive to the changes in knee OA in the early stage.Since magnetic resonance(MR)imaging can observe the early features of knee OA,the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered.Firstly,the knee MR images are preprocessed before training,including a region of interest clipping,slice selection,and data augmentation.Then the data set was divided by patient-level and the knee OA was classified by the deep transfer learning method based on the DenseNet201 model.The method divides the training process into two stages.The first stage freezes all the base layers and only trains the weights of the embedding neural networks.The second stage unfreezes part of the base layers and trains the unfrozen base layers and the weights of the embedding neural network.In this step,we design a block-by-block fine-tuning strategy for training based on the dense blocks,which improves detection accuracy.We have conducted training experiments with different depth modules,and the experimental results show that gradually adding more dense blocks in the fine-tuning can make the model obtain better detection performance than only training the embedded neural network layer.We achieve an accuracy of 0.921,a sensitivity of 0.960,a precision of 0.885,a specificity of 0.891,an F1-Score of 0.912,and an MCC of 0.836.The comparative experimental results on the OAI-ZIB dataset show that the proposed method outperforms the other detection methods with the accuracy of 92.1%.
文摘COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over the world.The healthcare sector of the world is facing great challenges tackling COVID cases.One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases.In this article,we propose a deep Convo-lutional Neural Network(CNN)based approach to detect COVID+(i.e.,patients with COVID-19),pneumonia and normal cases,from the chest X-ray images.COVID-19 detection from chest X-ray is suitable considering all aspects in compar-ison to Reverse Transcription Polymerase Chain Reaction(RT-PCR)and Computed Tomography(CT)scan.Several deep CNN models including VGG16,InceptionV3,DenseNet121,DenseNet201 and InceptionResNetV2 have been adopted in this pro-posed work.They have been trained individually to make particular predictions.Empirical results demonstrate that DenseNet201 provides overall better performance with accuracy,recall,F1-score and precision of 94.75%,96%,95%and 95%respec-tively.After careful comparison with results available in the literature,we have found to develop models with a higher reliability.All the studies were carried out using a publicly available chest X-ray(CXR)image data-set.
