Short-term fluctuation of intraocular pressure and influencing factors following intravitreal injection in patients with retinal vascular diseases

AIM:To investigate the patterns of short-term intraocular pressure(IOP)fluctuations and identify the contributing factors following intravitreal injection in patients with retinal vascular diseases.METHODS:Totally 81 patients were enrolled in this case control study.Eyes were categorized into 7 groups,including age-related macular degeneration(AMD),polypoidal choroidal vasculopathy(PCV),idiopathic choroidal neovascularization(CNV),proliferative diabetic retinopathy(PDR),diabetic macular edema(DME),macular edema secondary to branch(BVOME)and central(CVOME)retinal vein occlusion.IOP was measured in all patients using rebound tonometer at 7 preset time points perioperatively.Additionally,based on the administered medication,the eyes were classified into three treatment groups,including dexamethasone intravitreal implant(IVO),intravitreal conbercept(IVC),and intravitreal ranibizumab(IVR).To compare IOP values at various time points across groups,we employed one-way ANOVA,independent sample t-test or χ^(2) test and multivariate logistic regression analysis.RESULTS:Peak IOP values across all groups were observed at 40s,and 5min after intravitreal injection.Statistical differences in IOP were detected at the 5min among the 7 indication groups(F=2.50,P=0.029).When examing the impact of medications,the IVO group exhibited lower average IOP values at both 40s and 5min compared to the IVC and IVR groups(P<0.001;P=0.007).The IOP values at 40s and 5min were significantly higher in BVOME and CVOME group compared to non-retinal vein occlusionsecondary macular edema(RVOME)group(P<0.001).Multivariate logistic regression analysis further confirmed that IOP measurement at 40s was significantly higher in CVOME group than in non-RVOME group(OR=1.64,95%CI:1.09-2.47;P=0.018).CONCLUSION:Needle size plays a crucial role in the transient changes of IOP following intravitreal injection.Before administering intravitreal injection to patients with central retinal vein occlusion,it is essential to exclude any underlysing causes of increased IOP.

Detachable string magnetically controlled capsule endoscopy for the noninvasive diagnosis of esophageal diseases:A prospective,blind clinical study

BACKGROUND Traditional esophagogastroduodenoscopy(EGD),an invasive examination method,can cause discomfort and pain in patients.In contrast,magnetically controlled capsule endoscopy(MCE),a noninvasive method,is being applied for the detection of stomach and small intestinal diseases,but its application in treating esophageal diseases is not widespread.AIM To evaluate the safety and efficacy of detachable string MCE(ds-MCE)for the diagnosis of esophageal diseases.METHODS Fifty patients who had been diagnosed with esophageal diseases were pros-pectively recruited for this clinical study and underwent ds-MCE and conven-tional EGD.The primary endpoints included the sensitivity,specificity,positive predictive value,negative predictive value,and diagnostic accuracy of ds-MCE for patients with esophageal diseases.The secondary endpoints consisted of visualizing the esophageal and dentate lines,as well as the subjects'tolerance of the procedure.RESULTS Using EGD as the gold standard,the sensitivity,specificity,positive predictive value,negative predictive value,and diagnostic accuracy of ds-MCE for esophageal disease detection were 85.71%,86.21%,81.82%,89.29%,and 86%,respectively.ds-MCE was more comfortable and convenient than EGD was,with 80%of patients feeling that ds-MCE examination was very comfortable or comfortable and 50%of patients believing that detachable string v examination was very convenient.CONCLUSION This study revealed that ds-MCE has the same diagnostic effects as traditional EGD for esophageal diseases and is more comfortable and convenient than EGD,providing a novel noninvasive method for treating esophageal diseases.

Association of age at diagnosis of diabetes with subsequent risk of age-related ocular diseases and vision acuity

BACKGROUND The importance of age on the development of ocular conditions has been reported by numerous studies.Diabetes may have different associations with different stages of ocular conditions,and the duration of diabetes may affect the development of diabetic eye disease.While there is a dose-response relationship between the age at diagnosis of diabetes and the risk of cardiovascular disease and mortality,whether the age at diagnosis of diabetes is associated with incident ocular conditions remains to be explored.It is unclear which types of diabetes are more predictive of ocular conditions.AIM To examine associations between the age of diabetes diagnosis and the incidence of cataract,glaucoma,age-related macular degeneration(AMD),and vision acuity.METHODS Our analysis was using the UK Biobank.The cohort included 8709 diabetic participants and 17418 controls for ocular condition analysis,and 6689 diabetic participants and 13378 controls for vision analysis.Ocular diseases were identified using inpatient records until January 2021.Vision acuity was assessed using a chart.RESULTS During a median follow-up of 11.0 years,3874,665,and 616 new cases of cataract,glaucoma,and AMD,respectively,were identified.A stronger association between diabetes and incident ocular conditions was observed where diabetes was diagnosed at a younger age.Individuals with type 2 diabetes(T2D)diagnosed at<45 years[HR(95%CI):2.71(1.49-4.93)],45-49 years[2.57(1.17-5.65)],50-54 years[1.85(1.13-3.04)],or 50-59 years of age[1.53(1.00-2.34)]had a higher risk of AMD independent of glycated haemoglobin.T2D diagnosed<45 years[HR(95%CI):2.18(1.71-2.79)],45-49 years[1.54(1.19-2.01)],50-54 years[1.60(1.31-1.96)],or 55-59 years of age[1.21(1.02-1.43)]was associated with an increased cataract risk.T2D diagnosed<45 years of age only was associated with an increased risk of glaucoma[HR(95%CI):1.76(1.00-3.12)].HRs(95%CIs)for AMD,cataract,and glaucoma associated with type 1 diabetes(T1D)were 4.12(1.99-8.53),2.95(2.17-4.02),and 2.40(1.09-5.31),respectively.In multivariable-adjusted analysis,individuals with T2D diagnosed<45 years of age[β95%CI:0.025(0.009,0.040)]had a larger increase in LogMAR.Theβ(95%CI)for LogMAR associated with T1D was 0.044(0.014,0.073).CONCLUSION The younger age at the diagnosis of diabetes is associated with a larger relative risk of incident ocular diseases and greater vision loss.

Application of artificial intelligence in the diagnosis and treatment of Kawasaki disease

This editorial provides commentary on an article titled"Potential and limitationsof ChatGPT and generative artificial intelligence(AI)in medical safety education"recently published in the World Journal of Clinical Cases.AI has enormous potentialfor various applications in the field of Kawasaki disease(KD).One is machinelearning(ML)to assist in the diagnosis of KD,and clinical prediction models havebeen constructed worldwide using ML;the second is using a gene signalcalculation toolbox to identify KD,which can be used to monitor key clinicalfeatures and laboratory parameters of disease severity;and the third is using deeplearning(DL)to assist in cardiac ultrasound detection.The performance of the DLalgorithm is similar to that of experienced cardiac experts in detecting coronaryartery lesions to promoting the diagnosis of KD.To effectively utilize AI in thediagnosis and treatment process of KD,it is crucial to improve the accuracy of AIdecision-making using more medical data,while addressing issues related topatient personal information protection and AI decision-making responsibility.AIprogress is expected to provide patients with accurate and effective medicalservices that will positively impact the diagnosis and treatment of KD in thefuture.

Delayed diagnosis in inflammatory bowel disease:Time to consider solutions

In this editorial,we discuss a recently published manuscript by Blüthner et al in the World Journal of Gastroenterology,with a specific focus on the delayed diagnosis of inflammatory bowel disease(IBD).IBD,which includes Crohn's disease and ulcerative colitis,is a chronic intestinal disorder.A time lag may exist between the onset of inflammation and the appearance of signs and symptoms,potentially leading to an incorrect or delayed diagnosis,a situation referred to as the delayed diagnosis of IBD.Early diagnosis is crucial for effective patient treatment and prognosis,yet delayed diagnosis remains common.The reasons for delayed diagnosis of IBD are numerous and not yet fully understood.One key factor is the nonspecific nature of IBD symptoms,which can easily be mistaken for other conditions.Additionally,the lack of specific diagnostic methods for IBD contributes to these delays.Delayed diagnosis of IBD can result in numerous adverse consequences,including increased intestinal damage,fibrosis,a higher risk of colorectal cancer,and a decrease in the quality of life of the patient.Therefore,it is essential to diagnose IBD promptly by raising physician awareness,enhancing patient education,and developing new diagnostic methods.

Physician-dependent diagnosis delay in Crohn's disease:A pseudoproposition or not?

The challenge of diagnosis delay in inflammatory bowel disease(IBD)has emerged as a significant concern for both patients and healthcare professionals.The widely accepted notion that there is an extended time frame from the onset of symptoms to the definitive diagnosis is often attributed to the heterogeneity of IBD and the non-specificity of clinical manifestations.Specific to patients with Crohn’s disease,the issue of delayed diagnosis appears to be more pronounced across different regions globally.The intricate interplay of real-world factors has led to debates regarding the primary contributors to these diagnostic delays.Drawing a comparison solely between patients and physicians and implicating the latter as the predominant influence factor may fall into a simplistic either-or logical trap that may obscure the truth.This letter,grounded in published evidence,explores areas for improvement in a forthcoming paper within the field,hoping to pinpoint the culprit behind the diagnosis delay issue for IBD patients rather than simply attributing it to so-called“physician-dependent factors”.Our objective is to motivate healthcare providers and policymakers in relevant fields to reflect on strategies for addressing this problem to reduce diagnostic delays and enhance patient outcomes.

Differential diagnosis of Crohn’s disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning

BACKGROUND Crohn’s disease(CD)is often misdiagnosed as intestinal tuberculosis(ITB).However,the treatment and prognosis of these two diseases are dramatically different.Therefore,it is important to develop a method to identify CD and ITB with high accuracy,specificity,and speed.AIM To develop a method to identify CD and ITB with high accuracy,specificity,and speed.METHODS A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB.Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.RESULTS The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm^(-1) and 1234 cm^(-1) bands,and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy,specificity,and sensitivity of 91.84%,92.59%,and 90.90%,respectively,for the differential diagnosis of CD and ITB.CONCLUSION Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level,and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

Artificial intelligence in individualized retinal disease management

Owing to the rapid development of modern computer technologies,artificial intelligence(AI)has emerged as an essential instrument for intelligent analysis across a range of fields.AI has been proven to be highly effective in ophthalmology,where it is frequently used for identifying,diagnosing,and typing retinal diseases.An increasing number of researchers have begun to comprehensively map patients’retinal diseases using AI,which has made individualized clinical prediction and treatment possible.These include prognostic improvement,risk prediction,progression assessment,and interventional therapies for retinal diseases.Researchers have used a range of input data methods to increase the accuracy and dependability of the results,including the use of tabular,textual,or image-based input data.They also combined the analyses of multiple types of input data.To give ophthalmologists access to precise,individualized,and high-quality treatment strategies that will further optimize treatment outcomes,this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

Soluble ST2:A Novel Biomarker for Diagnosis and Prognosis of Cardiovascular Disease

The increasing incidence of cardiovascular disease(CVD)is a significant global health concern,affecting millions of individuals each year.Accurate diagnosis of acute CVD poses a formidable challenge,as misdiagnosis can significantly decrease patient survival rates.Traditional biomarkers have played a vital role in the diagnosis and prognosis of CVDs,but they can be influenced by various factors,such as age,sex,and renal function.Soluble ST2(sST2)is a novel biomarker that is closely associated with different CVDs.Its low reference change value makes it suitable for continuous measurement,unaffected by age,kidney function,and other confounding factors,facilitating risk stratification of CVDs.Furthermore,the combination of sST2 with other biomarkers can enhance diagnostic accuracy and prognostic value.This review aims to provide a comprehensive overview of sST2,focusing on its diagnostic and prognostic value as a myocardial marker for different types of CVDs and discussing the current limitations of sST2.

Mean nocturnal baseline impedance in gastro-esophageal reflux disease diagnosis:Should we strictly follow the Lyon 2 Consensus?

Clinical practice guidelines drive clinical practice and clinicians rely to them when trying to answer their most common questions.One of the most important position papers in the field of gastro-esophageal reflux disease(GERD)is the one produced by the Lyon Consensus.Recently an updated second version has been released.Mean nocturnal baseline impedance(MNBI)was proposed by the first Consensus to act as supportive evidence for GERD diagnosis.Originally a cut-off of 2292 Ohms was proposed,a value revised in the second edition.The updated Consensus recommended that an MNBI<1500 Ohms strongly suggests GERD while a value>2500 Ohms can be used to refute GERD.The proposed cut-offs move in the correct direction by diminishing the original cut-off,nevertheless they arise from a study of normal subjects where cut-offs were provided by measuring the mean value±2SD and not in symptomatic patients.However,data exist that even symptomatic patients with inconclusive disease or reflux hypersensitivity(RH)show lower MNBI values in comparison to normal subjects or patients with functional heartburn(FH).Moreover,according to the data,MNBI,even among symptomatic patients,is affected by age and body mass index.Also,various studies have proposed different cut-offs by using receiver operating characteristic curve analysis even lower than the one proposed.Finally,no information is given for patients submitted to on-proton pump inhibitors pH-impedance studies even if new and extremely important data now exist.Therefore,even if MNBI is an extremely important tool when trying to approach patients with reflux symptoms and could distinguish conclusive GERD from RH or FH,its values should be interpreted with caution.

Retinal capillary plexus in Parkinson’s disease using optical coherence tomography angiography

AIM:To evaluate the alterations of the retinal microvasculature and foveal avascular zone in patients with Parkinson’s disease(PD)using optical coherence tomography angiography(OCT-A).METHODS:A retrospective study of PD patients examined in the Ophthalmology Department of the General Hospital of Athens,“Georgios Gennimatas”from March 2021 to March 2022 was conducted.Totally 44 patients with PD were included and 18 healthy controls were examined,hence a total of 124 eyes were enrolled in the study.The foveal and parafoveal superficial and deep capillary plexus vascular density(fSCP-VD,fDCP-VD,pSCP-VD,pDCP-CD)and foveal avascular zone(FAZ)were quantified with OCTA.Optical coherence tomography(OCT)was used to measure macular thickness.Our statistical analysis was conducted by using a mixed effect linear regression model.RESULTS:After adjustment for age and gender,the mean parafoveal superficial capillary plexus vascular density(pSCP-VD)and mean parafoveal deep capillary plexus vascular density(pDCP-VD)were significantly decreased in individuals with PD(P<0.001 in both)by-2.35(95%CI-3.3,-1.45)and-7.5(95%CI-10.4,-4.6)respectively.fSCP-VD and fDCP-VD didn’t approach statistical significance.The FAZ area and perimeter were significantly decreased(P<0.001 in both)by-0.1 mm^(2)(95%CI-0.13,-0.07)and-0.49 mm^(2)(95%CI-0.66,-0.32)respectively.Circularity didn’t approach statistical significance.Central retinal thickness(CRT)was significantly decreased in individuals with PD(P<0.001)by-23.1μm(95%CI-30.2,-16)and temporal retinal thickness(TRT)was decreased(P=0.025)by-11μm(95%CI-22,-1.5)while nasal retinal thickness(NRT)only approached statistical significance(P=0.066).CONCLUSION:The mean pSCP-VD,pDCP-VD,CRT and TRT are significantly decreased and FAZ is altered in individuals with PD.These findings can be potentially used as biomarkers for the diagnosis and evaluation of early PD.

Retinal nerve fiber layer defects and chronic kidney disease:the Kailuan Eye Study

AIM:To investigate whether retinal nerve fiber layer defects(RNFLDs)is a potential risk factor for chronic kidney disease(CKD)in Chinese adults.METHODS:The Kailuan Eye Study was a populationbased study that included 14440 participants.All participants underwent detailed assessments,RNFLDs were diagnosed using color fundus photographs.RESULTS:Overall,12507 participants[8533 males(68.23%)]had complete systemic examination data and at least one evaluable fundus photograph.RNFLDs were found in 621 participants[5.0%;95%confidence interval(CI):4.6%-5.34%],and 70 cases of multiple RNFLDs were found(11.27%).After adjusting multiple factors,RNFLDs was significantly associated with CKD severity,the ORs of CKD stage 3,stage 4 and stage 5 were 1.698,4.167,and 9.512,respectively.Multiple RNFLDs were also associated with CKD severity after adjusting multiple factors,the ORs of CKD stage 3 and stage 5 were 4.465 and 11.833 respectively.Furthermore,2294 participants had CKD(18.34%,95%CI:17.68%-18.99%).After adjusting for other factors,CKD presence was significantly correlated with the presence of RNFLDs.CONCLUSION:The strongest risk factors for RNFLDs are CKD and hypertension.Conversely,RNFLDs can be an ocular feature in patients with CKD.Fundoscopy can help detect systemic diseases,and assessment for RNFLDs should be considered in CKD patients.

Association of autoimmune thyroid disease with type 1 diabetes mellitus and its ultrasonic diagnosis and management

As a common hyperglycemic disease,type 1 diabetes mellitus(T1DM)is a complicated disorder that requires a lifelong insulin supply due to the immunemediated destruction of pancreaticβcells.Although it is an organ-specific autoimmune disorder,T1DM is often associated with multiple other autoimmune disorders.The most prevalent concomitant autoimmune disorder occurring in T1DM is autoimmune thyroid disease(AITD),which mainly exhibits two extremes of phenotypes:hyperthyroidism[Graves'disease(GD)]and hypothyroidism[Hashimoto's thyroiditis,(HT)].However,the presence of comorbid AITD may negatively affect metabolic management in T1DM patients and thereby may increase the risk for potential diabetes-related complications.Thus,routine screening of thyroid function has been recommended when T1DM is diagnosed.Here,first,we summarize current knowledge regarding the etiology and pathogenesis mechanisms of both diseases.Subsequently,an updated review of the association between T1DM and AITD is offered.Finally,we provide a relatively detailed review focusing on the application of thyroid ultrasonography in diagnosing and managing HT and GD,suggesting its critical role in the timely and accurate diagnosis of AITD in T1DM.

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

Early non-invasive diagnosis of coronary heart disease(CHD)is critical.However,it is challenging to achieve accurate CHD diagnosis via detecting breath.In this work,heterostructured complexes of black phosphorus(BP)and two-dimensional carbide and nitride(MXene)with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy.A light-activated virtual sensor array(LAVSA)based on BP/Ti_(3)C_(2)Tx was prepared under photomodulation and further assembled into an instant gas sensing platform(IGSP).In addition,a machine learning(ML)algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD.Due to the synergistic effect of BP and Ti_(3)C_(2)Tx as well as photo excitation,the synthesized heterostructured complexes exhibited higher performance than pristine Ti_(3)C_(2)Tx,with a response value 26%higher than that of pristine Ti_(3)C_(2)Tx.In addition,with the help of a pattern recognition algorithm,LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols,ketones,aldehydes,esters,and acids.Meanwhile,with the assistance of ML,the IGSP achieved 69.2%accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients.In conclusion,an immediate,low-cost,and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD,which provided a generalized solution for diagnosing other diseases and other more complex application scenarios.

Intestinal Behçet's disease: A review of clinical diagnosis and treatment

Behçet's disease(BD)is a chronic inflammatory disorder prone to frequent re-currences,with a high predilection for intestinal involvement.However,the ef-ficacy and long-term effects of surgical treatment for intestinal BD are unknown.In the current issue of World J Gastrointest Surg,Park et al conducted a retrospec-tive analysis of 31 patients with intestinal BD who received surgical treatment.They found that elevated C-reactive protein levels and emergency surgery were poor prognostic factors for postoperative recurrence,emphasizing the adverse impact of severe inflammation on the prognosis of patients with intestinal BD.This work has clinical significance for evaluating the postoperative condition of intestinal BD.The editorial attempts to summarize the clinical diagnosis and treatment of intestinal BD,focusing on the impact of adverse factors on surgical outcomes.We hope this review will facilitate more precise postoperative management of patients with intestinal BD by clinicians.

Stem cell therapy in retinal diseases

Alteration of the outer retina leads to various diseases such as age-related macular degeneration or retinitis pigmentosa characterized by decreased visual acuity and ultimately blindness.Despite intensive research in the field of retinal disorders,there is currently no curative treatment.Several therapeutic approaches such as cell-based replacement and gene therapies are currently in development.In the context of cell-based therapies,different cell sources such as embryonic stem cells,induced pluripotent stem cells,or multipotent stem cells can be used for transplantation.In the vast majority of human clinical trials,retinal pigment epithelial cells and photoreceptors are the cell types considered for replacement cell therapies.In this review,we summarize the progress made in stem cell therapies ranging from the pre-clinical studies to clinical trials for retinal disease.

Convolutional Neural Network-Based Classificationof Multiple Retinal Diseases Using Fundus Images

Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful technique.The increase in retinal dis-eases is alarming as it may lead to permanent blindness if left untreated.Automa-tion of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time also.Several researchers have worked on automated retinal disease classification but restricted either to hand-crafted fea-ture selection or binary classification.This paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images.For this research,the data has been collected and combined from three distinct sources.The images are preprocessed for enhancing the details.Six layers of the convolutional neural network(CNN)are used for the automated feature extraction and classification of 20 retinal diseases.It is observed that the results are reliant on the number of classes.For binary classification(healthy vs.unhealthy),up to 100%accuracy has been achieved.When 16 classes are used(treating stages of a disease as a single class),93.3%accuracy,92%sensitivity and 93%specificity have been obtained respectively.For 20 classes(treating stages of the disease as separate classes),the accuracy,sensitivity and specificity have dropped to 92.4%,92%and 92%respectively.

Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot Learning

Objective To develop a few-shot learning(FSL) approach for classifying optical coherence tomography(OCT) images in patients with inherited retinal disorders(IRDs).Methods In this study, an FSL model based on a student–teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control samples.Results The FSL model achieved a total accuracy of 0.974–0.983, total sensitivity of 0.934–0.957, total specificity of 0.984–0.990, and total F1 score of 0.935–0.957, which were superior to the total accuracy of the baseline model of 0.943–0.954, total sensitivity of 0.866–0.886, total specificity of 0.962–0.971,and total F1 score of 0.859–0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves(AUC) of the receiver operating characteristic(ROC) curves in most subclassifications.Conclusion This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence.

Accuracy improvement for classifying retinal OCT images by diseases using deep learning-based selective denoising approach

In ophthalmology,retinal optical coherence tomography(OCT)images with noticeable structural features help identify human eyes as healthy or diseased.The recently hot arti ficial intelligence(AI)realized this recognition process automatically.However,speckle noise in the original retinal OCT image reduces the accuracy of disease classi fication.This study presents a timesaving approach based on deep learning to improve classi fication accuracy by removing the noise from the original dataset.Firstly,four pre-trained convolutional neural networks(CNNs)from the ImageNet Large Scale Visual Recognition Challenge(ILSVRC)were trained to classify the original images into two categories:The noise reduction required(NRR)and the noise-free(NF)images.Among the CNNs,VGG19 BN performed best with 98%accuracy and 99%recall.Then,we used the block-matching and 3D filtering(BM3D)algorithm to denoise the NRR images.Those noise-removed NRR and the NF images form the processed dataset.The quality of images in the dataset is prominently ameliorated after denoising,which is valid to improve the models'performance.The original and processed datasets were tested on the four pre-trained CNNs to evaluate the effectiveness of our proposed approach.We have compared the CNNs,and the results show the performance of the CNNs trained with the processed dataset is improved by an average of 2.04%,5.19%,and 5.10%under overall accuracy(OA),Macro F1-score,and Micro F1-score,respectively.Especially for DenseNet161,the OA is improved to 98.14%.Our proposed method demonstrates its effectiveness in improving classi fication accuracy and opens a new solution to reduce denoising time-consuming for large datasets.

Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

In agricultural engineering,the main challenge is on methodologies used for disease detection.The manual methods depend on the experience of the personal.Due to large variation in environmental condition,disease diagnosis and classification becomes a challenging task.Apart from the disease,the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background.In Cucurbita gourd family,the disease severity examination of leaf samples through computer vision,and deep learning methodologies have gained popularity in recent years.In this paper,a hybrid method based on Convolutional Neural Network(CNN)is proposed for automatic pumpkin leaf image classification.The Proposed Denoising and deep Convolutional Neural Network(CNN)method enhances the Pumpkin Leaf Pre-processing and diagnosis.Real time data base was used for training and testing of the proposed work.Investigation on existing pre-trained network Alexnet and googlenet was investigated is done to evaluate the performance of the pro-posed method.The system and computer simulations were performed using Matlab tool.