BACKGROUND Radiologic adjacent segment degeneration(ASDeg)can occur after spinal surgery.Adjacent segment disease(ASDis)is defined as the development of new clinical symptoms corresponding to radiographic changes adja...BACKGROUND Radiologic adjacent segment degeneration(ASDeg)can occur after spinal surgery.Adjacent segment disease(ASDis)is defined as the development of new clinical symptoms corresponding to radiographic changes adjacent to the level of previous spinal surgery.Greater pre-existing ASDeg is generally considered to result in more severe ASDis;nonetheless,whether the ASDeg status before index surgery influences the postoperative risk of revision surgery due to ASDis warrants investigation.AIM To identify possible risk factors for ASDis and verify the concept that greater preexisting ASDeg leads to more severe ASDis.METHODS Data from 212 patients who underwent posterior decompression with Dynesys stabilization from January 2006 to June 2016 were retrospectively analyzed.Patients who underwent surgery for ASDis were categorized as group A(n=13),whereas those who did not were classified as group B(n=199).Survival analysis and Cox proportional hazards models were used to compare the modified Pfirrmann grade,University of California-Los Angeles grade,body mass index,number of Dynesys-instrumented levels,and age.RESULTS The mean time of reoperation was 7.22(1.65–11.84)years in group A,and the mean follow-up period was 6.09(0.10–12.76)years in group B.No significant difference in reoperation risk was observed:Modified Pfirrmann grade 3 vs 4(P=0.53)or 4 vs 5(P=0.46)for the upper adjacent disc,University of California-Los Angeles grade 2 vs 3 for the upper adjacent segment(P=0.66),age of<60 vs>60 years(P=0.9),body mass index<25 vs>25 kg/m2(P=0.3),and sex(P=0.8).CONCLUSION Greater preexisting upper ASDeg was not associated with a higher rate of reoperation for ASDis after Dynesys surgery.Being overweight tended to increase reoperation risk after Dynesys surgery for ASDis.展开更多
Study Design: This is a retrospective cohort study using data from the adult spinal deformity (ASD) database of a single institution. Purpose: To investigate the incidence of proximal junctional failure and distal jun...Study Design: This is a retrospective cohort study using data from the adult spinal deformity (ASD) database of a single institution. Purpose: To investigate the incidence of proximal junctional failure and distal junctional failure (DJF) after ASD surgery with a lower instrumented vertebra (LIV) at L5. Overview of Literature: Spinopelvic fixation from the lower thoracic vertebra to the pelvis is the current gold standard treatment for ASD. However, the LIV at L5 is acceptable in some cases. Methods: Fifty-six patients who underwent corrective surgery for ASD with LIV at L5 were included. The upper instrumented vertebra (UIV) was T7 in one patient, T9 in 14, T10 in three, T11 in four, T12 in eight, L1 in 10, and L2 in 16. Regarding clinical parameters, age, sex, curve types of Scoliosis Research Society-Schwab classification, number of levels fused, follow-up period, hip bone mallow density, revision surgery rate, and radiographic measurements were compared between the T (UIV: T7 - 10) and TL (UIV: T11 - L2) groups. Results: The revision surgery rate was 19.6% overall. In the T and TL groups, it was 27.8%, and 15.8%, respectively (p = 0.305). The rate of DJF in the T group (33.3%) was significantly higher than in the TL group (5.3%). The rate of proximal junctional kyphosis in the T group (55.6%) was higher than in the TL group (28.9%), with no significant difference. The mean global alignment, sagittal vertical axis, and C7 plumb line-central sacral vertical line were not different between both groups. Conclusions: ASD surgery with LIV set at L5 and UIV set at the thoracic vertebrae (T7 - T10) has a risk of adjacent segment disease.展开更多
The prone transpsoas approach is a relatively new technique to correct segmental kyphosis and global sagittal imbalance in a minimally invasive fashion. Here, we provide a detailed case report using the prone transpso...The prone transpsoas approach is a relatively new technique to correct segmental kyphosis and global sagittal imbalance in a minimally invasive fashion. Here, we provide a detailed case report using the prone transpsoas approach to address adjacent segment disease and flatback deformity. This technique allows considerable restoration of segmental lordosis with lateral interbody placement and posterior decompression and fusion using a single position approach. Our experience with the surgical technique and the advantages and challenges unique to this approach are discussed.展开更多
In the emerging field of image segmentation,Fully Convolutional Networks(FCNs)have recently become prominent.However,their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparamete...In the emerging field of image segmentation,Fully Convolutional Networks(FCNs)have recently become prominent.However,their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters,which can often be a cumbersome manual task.The main aim of this study is to propose a more efficient,less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images.To this end,our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network(FCEDN).The optimization is handled by a novel Genetic Grey Wolf Optimization(G-GWO)algorithm.This algorithm employs the Genetic Algorithm(GA)to generate a diverse set of initial positions.It leverages Grey Wolf Optimization(GWO)to fine-tune these positions within the discrete search space.Testing on the Indian Diabetic Retinopathy Image Dataset(IDRiD),Diabetic Retinopathy,Hypertension,Age-related macular degeneration and Glacuoma ImageS(DR-HAGIS),and Ocular Disease Intelligent Recognition(ODIR)datasets showed that the G-GWO method outperformed four other variants of GWO,GA,and PSO-based hyperparameter optimization techniques.The proposed model achieved impressive segmentation results,with accuracy rates of 98.5%for IDRiD,98.7%for DR-HAGIS,and 98.4%,98.8%,and 98.5%for different sub-datasets within ODIR.These results suggest that the proposed hyperparameter-optimized FCEDN model,driven by the G-GWO algorithm,is more efficient than recent deep-learning models for image segmentation tasks.It thereby presents the potential for increased automation and accuracy in the segmentation of fundus images,mitigating the need for extensive manual hyperparameter adjustments.展开更多
Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)method...Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).展开更多
BACKGROUND The association between congenital heart disease and chronic kidney disease is well known.Various mechanisms of kidney damage associated with congenital heart disease have been established.The etiology of k...BACKGROUND The association between congenital heart disease and chronic kidney disease is well known.Various mechanisms of kidney damage associated with congenital heart disease have been established.The etiology of kidneydisease has commonly been considered to be secondary to focal segmental glomerulosclerosis(FSGS),however,this has only been demonstrated in case reports and not in observational or clinical trials.AIM To identify baseline and clinical characteristics,as well as the findings in kidney biopsies of patients with congenital heart disease in our hospital.METHODS This is a retrospective observational study conducted at the Nephrology Depart-ment of the National Institute of Cardiology“Ignacio Chávez”.All patients over 16 years old who underwent percutaneous kidney biopsy from January 2000 to January 2023 with congenital heart disease were included in the study.RESULTS Ten patients with congenital heart disease and kidney biopsy were found.The average age was 29.00 years±15.87 years with pre-biopsy proteinuria of 6193 mg/24 h±6165 mg/24 h.The most common congenital heart disease was Fallot’s tetralogy with 2 cases(20%)and ventricular septal defect with 2(20%)cases.Among the 10 cases,one case of IgA nephropathy and one case of membranoproliferative glomerulonephritis associated with immune complexes were found,receiving specific treatment after histopathological diagnosis,delaying the initiation of kidney replacement therapy.Among remaining 8 cases(80%),one case of FSGS with perihilar variety was found,while the other 7 cases were non-specific FSGS.CONCLUSION Determining the cause of chronic kidney disease can help in delaying the need for kidney replacement therapy.In 2 out of 10 patients in our study,interventions were performed,and initiation of kidney replacement therapy was delayed.Prospective studies are needed to determine the usefulness of kidney biopsy in patients with congenital heart disease.展开更多
In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segme...In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.展开更多
The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non...The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.展开更多
BACKGROUND Although minimal change disease(MCD)and focal segmental glomerulosclerosis(FSGS)have been described as two separate forms of nephrotic syndrome(NS),they are not completely independent.We report a case of a ...BACKGROUND Although minimal change disease(MCD)and focal segmental glomerulosclerosis(FSGS)have been described as two separate forms of nephrotic syndrome(NS),they are not completely independent.We report a case of a patient transitioning from MCD to FSGS,review the literature,and explore the relationship between the two diseases.CASE SUMMARY A 42-year-old male welder,presenting with lower extremity edema and elevated serum creatinine,was diagnosed with NS and end-stage kidney disease(ESKD)based on laboratory test results.The patient had undergone a kidney biopsy for NS 20 years previously,which indicated MCD,and a second recent kidney biopsy suggested FSGS.The patient was an electric welder with excessive levels of cadmium and lead in his blood.Consequently,we suspect that his aggravated pathology and occurrence of ESKD were related to metal nephrotoxicity.The patient eventually received kidney replacement therapy and quit his job which involved long-term exposure to metals.During the 1-year follow-up period,the patient was negative for metal elements in the blood and urine and recovered partial kidney function.CONCLUSION MCD and FSGS may be different stages of the same disease.The transition from MCD to FSGS in this case indicates disease progression,which may be related to excessive metal contaminants caused by the patient’s occupation.展开更多
Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor younger.There is a need to develop new technologies andmethods to deal with this disease.Many researchers hav...Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor younger.There is a need to develop new technologies andmethods to deal with this disease.Many researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a challenge.Several different types of angiograms are adopted for CAD diagnosis.This paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art results.We have collected the 2D X-ray images from the hospital,and the proposed model has been applied to them.Image augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s size.Also,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high accuracy.As the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.展开更多
Crop protection is a great obstacle to food safety,with crop diseases being one of the most serious issues.Plant diseases diminish the quality of crop yield.To detect disease spots on grape leaves,deep learning techno...Crop protection is a great obstacle to food safety,with crop diseases being one of the most serious issues.Plant diseases diminish the quality of crop yield.To detect disease spots on grape leaves,deep learning technology might be employed.On the other hand,the precision and efficiency of identification remain issues.The quantity of images of ill leaves taken from plants is often uneven.With an uneven collection and few images,spotting disease is hard.The plant leaves dataset needs to be expanded to detect illness accurately.A novel hybrid technique employing segmentation,augmentation,and a capsule neural network(CapsNet)is used in this paper to tackle these challenges.The proposed method involves three phases.First,a graph-based technique extracts leaf area from a plant image.The second step expands the dataset using an Efficient Generative Adversarial Network E-GAN.Third,a CapsNet identifies the illness and stage.The proposed work has experimented on real-time grape leaf images which are captured using an SD1000 camera and PlantVillage grape leaf datasets.The proposed method achieves an effective classification of accuracy for disease type and disease stages detection compared to other existing models.展开更多
Since the onset of the coronavirus disease 2019(COVID-19)pandemic,numerous reports of associated ocular manifestations have emerged.Involvement of nearly every ocular structure has been reported.The aim of this review...Since the onset of the coronavirus disease 2019(COVID-19)pandemic,numerous reports of associated ocular manifestations have emerged.Involvement of nearly every ocular structure has been reported.The aim of this review is to describe various manifestations of the severe acute respiratory disease coronavirus 2(SARS-CoV-2)virus on the eye,focused primarily on the posterior segment,and discuss proposed pathophysiology and mechanisms of involvement of these ophthalmic structures.Proposed mechanisms of ocular involvement of COVID-19 parallel those of systemic manifestations and include viral and other microbial reactivation,primary infection,inflammation,and thromboembolism.Viral reactivation of Herpes Simplex Virus,Varicella Zoster Virus,and Epstein-Barr Virus has been presumed in cases of acute retinal necrosis(ARN),while bacterial and parasitic infections have also been reported albeit less commonly.Primary infection has also been thought to contribute to various inflammatory presentations.Thromboembolic manifestations include various retinal artery and vein occlusions among other less visually significant signs such as cotton wool spots.Cranial neuropathies including optic neuropathy,as well as optic neuritis have also been widely reported.COVID-19 vaccines are increasingly associated with ocular signs and syndromes.In this paper we explore various reported ophthalmic manifestations of COVID-19 infection,primarily involving the posterior segment.Given the novel nature of the virus and overall paucity of cases,further study is required to better elucidate the causal relationship between the virus and its ophthalmologic effects.展开更多
针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectiona...针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectional encoder representation from transformers)预训练语言模型进行文本向量化表示;通过双向长短时记忆网络(Bidirectional long short-term memory network,BiLSTM)获取上下文语义特征;由条件随机场(Conditional random field,CRF)输出全局最优标签序列。基于此,在CRF层后加入畜禽疫病领域词典进行分词匹配修正,减少在分词过程中出现的疫病名称及短语等造成的歧义切分,进一步提高了分词准确率。实验结果表明,结合词典匹配的BERT-BiLSTM-CRF模型在羊常见疫病文本数据集上的F1值为96.38%,与jieba分词器、BiLSTM-Softmax模型、BiLSTM-CRF模型、未结合词典匹配的本文模型相比,分别提升11.01、10.62、8.3、0.72个百分点,验证了方法的有效性。与单一语料相比,通用语料PKU和羊常见疫病文本数据集结合的混合语料,能够同时对畜禽疫病专业术语及疫病文本中常用词进行准确切分,在通用语料及疫病文本数据集上F1值都达到95%以上,具有较好的模型泛化能力。该方法可用于畜禽疫病文本分词。展开更多
基金The study was approved by our institutional review board,Research Ethics Committee China Medical University and Hospital,Taichung,Taiwan(Protocol No.:CMUH108-REC2-133).
文摘BACKGROUND Radiologic adjacent segment degeneration(ASDeg)can occur after spinal surgery.Adjacent segment disease(ASDis)is defined as the development of new clinical symptoms corresponding to radiographic changes adjacent to the level of previous spinal surgery.Greater pre-existing ASDeg is generally considered to result in more severe ASDis;nonetheless,whether the ASDeg status before index surgery influences the postoperative risk of revision surgery due to ASDis warrants investigation.AIM To identify possible risk factors for ASDis and verify the concept that greater preexisting ASDeg leads to more severe ASDis.METHODS Data from 212 patients who underwent posterior decompression with Dynesys stabilization from January 2006 to June 2016 were retrospectively analyzed.Patients who underwent surgery for ASDis were categorized as group A(n=13),whereas those who did not were classified as group B(n=199).Survival analysis and Cox proportional hazards models were used to compare the modified Pfirrmann grade,University of California-Los Angeles grade,body mass index,number of Dynesys-instrumented levels,and age.RESULTS The mean time of reoperation was 7.22(1.65–11.84)years in group A,and the mean follow-up period was 6.09(0.10–12.76)years in group B.No significant difference in reoperation risk was observed:Modified Pfirrmann grade 3 vs 4(P=0.53)or 4 vs 5(P=0.46)for the upper adjacent disc,University of California-Los Angeles grade 2 vs 3 for the upper adjacent segment(P=0.66),age of<60 vs>60 years(P=0.9),body mass index<25 vs>25 kg/m2(P=0.3),and sex(P=0.8).CONCLUSION Greater preexisting upper ASDeg was not associated with a higher rate of reoperation for ASDis after Dynesys surgery.Being overweight tended to increase reoperation risk after Dynesys surgery for ASDis.
文摘Study Design: This is a retrospective cohort study using data from the adult spinal deformity (ASD) database of a single institution. Purpose: To investigate the incidence of proximal junctional failure and distal junctional failure (DJF) after ASD surgery with a lower instrumented vertebra (LIV) at L5. Overview of Literature: Spinopelvic fixation from the lower thoracic vertebra to the pelvis is the current gold standard treatment for ASD. However, the LIV at L5 is acceptable in some cases. Methods: Fifty-six patients who underwent corrective surgery for ASD with LIV at L5 were included. The upper instrumented vertebra (UIV) was T7 in one patient, T9 in 14, T10 in three, T11 in four, T12 in eight, L1 in 10, and L2 in 16. Regarding clinical parameters, age, sex, curve types of Scoliosis Research Society-Schwab classification, number of levels fused, follow-up period, hip bone mallow density, revision surgery rate, and radiographic measurements were compared between the T (UIV: T7 - 10) and TL (UIV: T11 - L2) groups. Results: The revision surgery rate was 19.6% overall. In the T and TL groups, it was 27.8%, and 15.8%, respectively (p = 0.305). The rate of DJF in the T group (33.3%) was significantly higher than in the TL group (5.3%). The rate of proximal junctional kyphosis in the T group (55.6%) was higher than in the TL group (28.9%), with no significant difference. The mean global alignment, sagittal vertical axis, and C7 plumb line-central sacral vertical line were not different between both groups. Conclusions: ASD surgery with LIV set at L5 and UIV set at the thoracic vertebrae (T7 - T10) has a risk of adjacent segment disease.
文摘The prone transpsoas approach is a relatively new technique to correct segmental kyphosis and global sagittal imbalance in a minimally invasive fashion. Here, we provide a detailed case report using the prone transpsoas approach to address adjacent segment disease and flatback deformity. This technique allows considerable restoration of segmental lordosis with lateral interbody placement and posterior decompression and fusion using a single position approach. Our experience with the surgical technique and the advantages and challenges unique to this approach are discussed.
基金supported in part by the National Natural Science Foundation of China under Grant 11527801 and 41706201.
文摘In the emerging field of image segmentation,Fully Convolutional Networks(FCNs)have recently become prominent.However,their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters,which can often be a cumbersome manual task.The main aim of this study is to propose a more efficient,less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images.To this end,our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network(FCEDN).The optimization is handled by a novel Genetic Grey Wolf Optimization(G-GWO)algorithm.This algorithm employs the Genetic Algorithm(GA)to generate a diverse set of initial positions.It leverages Grey Wolf Optimization(GWO)to fine-tune these positions within the discrete search space.Testing on the Indian Diabetic Retinopathy Image Dataset(IDRiD),Diabetic Retinopathy,Hypertension,Age-related macular degeneration and Glacuoma ImageS(DR-HAGIS),and Ocular Disease Intelligent Recognition(ODIR)datasets showed that the G-GWO method outperformed four other variants of GWO,GA,and PSO-based hyperparameter optimization techniques.The proposed model achieved impressive segmentation results,with accuracy rates of 98.5%for IDRiD,98.7%for DR-HAGIS,and 98.4%,98.8%,and 98.5%for different sub-datasets within ODIR.These results suggest that the proposed hyperparameter-optimized FCEDN model,driven by the G-GWO algorithm,is more efficient than recent deep-learning models for image segmentation tasks.It thereby presents the potential for increased automation and accuracy in the segmentation of fundus images,mitigating the need for extensive manual hyperparameter adjustments.
文摘Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).
文摘BACKGROUND The association between congenital heart disease and chronic kidney disease is well known.Various mechanisms of kidney damage associated with congenital heart disease have been established.The etiology of kidneydisease has commonly been considered to be secondary to focal segmental glomerulosclerosis(FSGS),however,this has only been demonstrated in case reports and not in observational or clinical trials.AIM To identify baseline and clinical characteristics,as well as the findings in kidney biopsies of patients with congenital heart disease in our hospital.METHODS This is a retrospective observational study conducted at the Nephrology Depart-ment of the National Institute of Cardiology“Ignacio Chávez”.All patients over 16 years old who underwent percutaneous kidney biopsy from January 2000 to January 2023 with congenital heart disease were included in the study.RESULTS Ten patients with congenital heart disease and kidney biopsy were found.The average age was 29.00 years±15.87 years with pre-biopsy proteinuria of 6193 mg/24 h±6165 mg/24 h.The most common congenital heart disease was Fallot’s tetralogy with 2 cases(20%)and ventricular septal defect with 2(20%)cases.Among the 10 cases,one case of IgA nephropathy and one case of membranoproliferative glomerulonephritis associated with immune complexes were found,receiving specific treatment after histopathological diagnosis,delaying the initiation of kidney replacement therapy.Among remaining 8 cases(80%),one case of FSGS with perihilar variety was found,while the other 7 cases were non-specific FSGS.CONCLUSION Determining the cause of chronic kidney disease can help in delaying the need for kidney replacement therapy.In 2 out of 10 patients in our study,interventions were performed,and initiation of kidney replacement therapy was delayed.Prospective studies are needed to determine the usefulness of kidney biopsy in patients with congenital heart disease.
基金supported by the National Natural Science Foundation of China (31501229)the Chinese Academy of Agricultural Sciences Innovation Project (CAAS-ASTIP2017-AII)the Special Research Funds for Basic Scientific Research in Central Public Welfare Research Institutes, China (JBYW-AII-2017-05)
文摘In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.
基金financially supported by the Deanship of Scientific Research,Qassim University,Saudi Arabia for funding the publication of this project.
文摘The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.
文摘BACKGROUND Although minimal change disease(MCD)and focal segmental glomerulosclerosis(FSGS)have been described as two separate forms of nephrotic syndrome(NS),they are not completely independent.We report a case of a patient transitioning from MCD to FSGS,review the literature,and explore the relationship between the two diseases.CASE SUMMARY A 42-year-old male welder,presenting with lower extremity edema and elevated serum creatinine,was diagnosed with NS and end-stage kidney disease(ESKD)based on laboratory test results.The patient had undergone a kidney biopsy for NS 20 years previously,which indicated MCD,and a second recent kidney biopsy suggested FSGS.The patient was an electric welder with excessive levels of cadmium and lead in his blood.Consequently,we suspect that his aggravated pathology and occurrence of ESKD were related to metal nephrotoxicity.The patient eventually received kidney replacement therapy and quit his job which involved long-term exposure to metals.During the 1-year follow-up period,the patient was negative for metal elements in the blood and urine and recovered partial kidney function.CONCLUSION MCD and FSGS may be different stages of the same disease.The transition from MCD to FSGS in this case indicates disease progression,which may be related to excessive metal contaminants caused by the patient’s occupation.
文摘Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor younger.There is a need to develop new technologies andmethods to deal with this disease.Many researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a challenge.Several different types of angiograms are adopted for CAD diagnosis.This paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art results.We have collected the 2D X-ray images from the hospital,and the proposed model has been applied to them.Image augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s size.Also,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high accuracy.As the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.
文摘Crop protection is a great obstacle to food safety,with crop diseases being one of the most serious issues.Plant diseases diminish the quality of crop yield.To detect disease spots on grape leaves,deep learning technology might be employed.On the other hand,the precision and efficiency of identification remain issues.The quantity of images of ill leaves taken from plants is often uneven.With an uneven collection and few images,spotting disease is hard.The plant leaves dataset needs to be expanded to detect illness accurately.A novel hybrid technique employing segmentation,augmentation,and a capsule neural network(CapsNet)is used in this paper to tackle these challenges.The proposed method involves three phases.First,a graph-based technique extracts leaf area from a plant image.The second step expands the dataset using an Efficient Generative Adversarial Network E-GAN.Third,a CapsNet identifies the illness and stage.The proposed work has experimented on real-time grape leaf images which are captured using an SD1000 camera and PlantVillage grape leaf datasets.The proposed method achieves an effective classification of accuracy for disease type and disease stages detection compared to other existing models.
文摘Since the onset of the coronavirus disease 2019(COVID-19)pandemic,numerous reports of associated ocular manifestations have emerged.Involvement of nearly every ocular structure has been reported.The aim of this review is to describe various manifestations of the severe acute respiratory disease coronavirus 2(SARS-CoV-2)virus on the eye,focused primarily on the posterior segment,and discuss proposed pathophysiology and mechanisms of involvement of these ophthalmic structures.Proposed mechanisms of ocular involvement of COVID-19 parallel those of systemic manifestations and include viral and other microbial reactivation,primary infection,inflammation,and thromboembolism.Viral reactivation of Herpes Simplex Virus,Varicella Zoster Virus,and Epstein-Barr Virus has been presumed in cases of acute retinal necrosis(ARN),while bacterial and parasitic infections have also been reported albeit less commonly.Primary infection has also been thought to contribute to various inflammatory presentations.Thromboembolic manifestations include various retinal artery and vein occlusions among other less visually significant signs such as cotton wool spots.Cranial neuropathies including optic neuropathy,as well as optic neuritis have also been widely reported.COVID-19 vaccines are increasingly associated with ocular signs and syndromes.In this paper we explore various reported ophthalmic manifestations of COVID-19 infection,primarily involving the posterior segment.Given the novel nature of the virus and overall paucity of cases,further study is required to better elucidate the causal relationship between the virus and its ophthalmologic effects.
文摘针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectional encoder representation from transformers)预训练语言模型进行文本向量化表示;通过双向长短时记忆网络(Bidirectional long short-term memory network,BiLSTM)获取上下文语义特征;由条件随机场(Conditional random field,CRF)输出全局最优标签序列。基于此,在CRF层后加入畜禽疫病领域词典进行分词匹配修正,减少在分词过程中出现的疫病名称及短语等造成的歧义切分,进一步提高了分词准确率。实验结果表明,结合词典匹配的BERT-BiLSTM-CRF模型在羊常见疫病文本数据集上的F1值为96.38%,与jieba分词器、BiLSTM-Softmax模型、BiLSTM-CRF模型、未结合词典匹配的本文模型相比,分别提升11.01、10.62、8.3、0.72个百分点,验证了方法的有效性。与单一语料相比,通用语料PKU和羊常见疫病文本数据集结合的混合语料,能够同时对畜禽疫病专业术语及疫病文本中常用词进行准确切分,在通用语料及疫病文本数据集上F1值都达到95%以上,具有较好的模型泛化能力。该方法可用于畜禽疫病文本分词。
