Diagnosis Of Disease Research Articles

Ultra-minimally invasive endoscopic techniques and colorectal diseases: Current status and its future

Colorectal diseases are increasing due to altered lifestyle, genetic, and environmental factors. Colonoscopy plays an important role in diagnosis. Advances in colonoscope (ultrathin scope, magnetic scope, capsule) and technological gadgets (Balloon assisted scope, third eye retroscope, NaviAid G-EYE, dye-based chromoendoscopy, virtual chromoendoscopy, narrow band imaging, i-SCAN, etc. ) have made colonoscopy more comfortable and efficient. Now in-vivo microscopy can be performed using confocal laser endomicroscopy, optical coherence tomography, spectroscopy, etc. Besides developments in diagnostic colonoscopy, therapeutic colonoscopy has improved to manage lower gastrointestinal tract bleeding, obstruction, perforations, resection polyps, and early colorectal cancers. The introduction of combined endo-laparoscopic surgery and robotic endoscopic surgery has made these interventions feasible. The role of artificial intelligence in the diagnosis and management of colorectal diseases is also increasing day by day. Hence, this article is to review cutting-edge developments in endoscopic principles for the management of colorectal diseases.

Computed tomography three-dimensional reconstruction in the diagnosis of bleeding small intestinal polyps: A case report

BACKGROUND Computed tomography (CT) small bowel three-dimensional (3D) reconstruction is a powerful tool for the diagnosis of small bowel disease and can clearly show the intestinal lumen and wall as well as the outside structure of the wall. The horizontal axis position can show the best adjacent intestinal tube and the lesion between the intestinal tubes, while the coronal position can show the overall view of the small bowel. The ileal end of the localization of the display of excellent, and easy to quantitative measurement of the affected intestinal segments, the sagittal position for the rectum and the pre-sacral lesions show the best, for the discovery of fistulae is also helpful. Sagittal view can show rectal and presacral lesions and is useful for fistula detection. It is suitable for the assessment of inflammatory bowel disease, such as assessment of disease severity and diagnosis and differential diagnosis of the small bowel and mesenteric space-occupying lesions as well as the judgment of small bowel obstruction points. CASE SUMMARY Bleeding caused by small intestinal polyps is often difficult to diagnose in clinical practice. This study reports a 29-year-old male patient who was admitted to the hospital with black stool and abdominal pain for 3 months. Using the combination of CT-3D reconstruction and capsule endoscopy, the condition was diagnosed correctly, and the polyps were removed using single-balloon enteroscopy-endoscopic retrograde cholangiopancreatography without postoperative complications. CONCLUSION The role of CT-3D in gastrointestinal diseases was confirmed. CT-3D can assist in the diagnosis and treatment of gastrointestinal diseases in combination with capsule endoscopy and small intestinal microscopy.

Handheld echocardiography for the screening and diagnosis of rheumatic heart disease: a systematic review to inform WHO guidelines

Early detection and diagnosis of acute rheumatic fever and rheumatic heart disease are key to preventing progression, and echocardiography has an important diagnostic role. Standard echocardiography might not be feasible in high-prevalence regions due to its high cost, complexity, and time requirement. Handheld echocardiography might be an easy-to-use, low-cost alternative, but its performance in screening for and diagnosing acute rheumatic fever and rheumatic heart disease needs further investigation. In this systematic review and meta-analysis, we searched Embase, MEDLINE, LILACS, and Conference Proceedings Citation Index-Science up to Feb 9, 2024, for studies on the screening and diagnosis of acute rheumatic fever and rheumatic heart disease using handheld echocardiography (index test) or standard echocardiography or auscultation (reference tests) in high-prevalence areas. We included all studies with useable data in which the diagnostic performance of the index test was assessed against a reference test. Data on test accuracy in diagnosing rheumatic heart disease, acute rheumatic fever, or carditis with acute rheumatic fever (primary outcomes) were extracted from published articles or calculated, with authors contacted as necessary. Quality of evidence was appraised using GRADE and QUADAS-2 criteria. We summarised diagnostic accuracy statistics (including sensitivity and specificity) and estimated 95% CIs using a bivariate random-effects model (or univariate random-effects models for analyses including three or fewer studies). Area under the curve (AUC) was calculated from summary receiver operating characteristic curves. Heterogeneity was assessed by visual inspection of plots. This study was registered with PROSPERO (CRD42022344081). Out of 4868 records we identified 11 studies, and two additional reports, comprising 15 578 unique participants. Pooled data showed that handheld echocardiography had high sensitivity (0·87 [95% CI 0·76-0·93]), specificity (0·98 [0·71-1·00]), and overall high accuracy (AUC 0·94 [0·84-1·00]) for diagnosing rheumatic heart disease when compared with standard echocardiography (two studies; moderate certainty of evidence), with better performance for diagnosing definite compared with borderline rheumatic heart disease. High sensitivity (0·79 [0·73-0·84]), specificity (0·85 [0·80-0·89]), and overall accuracy (AUC 0·90 [0·85-0·94]) for screening rheumatic heart disease was observed when pooling data of handheld echocardiography versus standard echocardiography (seven studies; high certainty of evidence). Most studies had a low risk of bias overall. Some heterogeneity was observed for sensitivity and specificity across studies, possibly driven by differences in the prevalence and severity of rheumatic heart disease, and level of training or expertise of non-expert operators. Handheld echocardiography has a high accuracy and diagnostic performance when compared with standard echocardiography for diagnosing and screening of rheumatic heart disease in high-prevalence areas. World Health Organization. For the Chinese, French, Italian, Persian, Portuguese, Spanish and Urdu translations of the abstract see Supplementary Materials section.

Nanopore single molecule ATP detection based on the dual effects of specific capturing and signal amplification

Adenosine triphosphate (ATP) plays a crucial role in energy metabolism. Some serious diseases such as Alzheimer’s disease are related to abnormal ATP concentrations. Fast, sensitive, and specific ATP detection is important and challenging in disease diagnosis and biomedical research. In present study, conical nanopore was designed and employed to detect ATP at single molecule level. For overcoming the inherent defects such as non-selectivity and low sensitivity of nanopore in sensing small target molecule, two collaborative improvements of specific identification and signal amplification were proposed. We first used split aptamer capture probes (H1 and S1) to anchor ATP molecule, and then used auxiliary probes (H2 and H3) to form a long-nicked duplex ATP-DNA concatemer by hybridization chain reaction (HCR). Referring ATP-DNA concatemer rather than native ATP to nanopore sensor, characteristic current signals with high signal-to-noise ratio were obtained. By such indirect way, specific and sensitive ATP detection was realized with a linear range from 20 pM to 5 nM and a detection limit of 3.9 pM (S/N = 3). Additionally, the sensor exhibited many merits such as simplify, anti-interference, and especially allowing ATP detection in real sample. So, we believe that the nanopore sensor has good application prospects in screening and management of ATP-related diseases.

A dual-signal ECL-RET immunosensor based on VC-g-C3N4 and Ru-Zr-MOF for the detection of β2-MG

This work proposes a novel dual-signal electrochemiluminescence (ECL) immunosensor based on energy resonance transfer (RET) for the detection of β2-Microglobulin (β2-MG). Introducing carbon vacancy into g-C3N4 (VC-g-C3N4) can lead to the narrowing of the band gap and the reduction of LUMO, thereby significantly enhancing the ECL efficiency of the material. Meanwhile, Zr-MOF can effectively load Ru(bpy)32+ (Ru-Zr-MOF), enabling its stable existence on the electrode. The ECL emission spectrum of VC-g-C3N4 overlaps well with the fluorescence excitation spectrum of Ru-Zr-MOF, indicating an effective ECL-RET effect between them. As the concentration of β2-MG increases, the intensity of anodic ECL signal gradually increases, while the intensity of cathodic ECL signal gradually decreases. The difference between the cathodic and anodic ECL signals has a good linear relationship within the concentration range from 0.2 pg/mL to 200 ng/mL, with a detection limit of 27.73 fg/mL. The constructed immunosensor can be used for practical analysis in human serum. The proposed sensor has great potential in clinical detection of β2-MG, providing new insights for biomolecular detection and disease diagnosis.

Analyzing electroencephalograph signals for early Alzheimer’s disease detection: deep learning vs. traditional machine learning approaches

Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machine learning methods and advanced deep learning strategies for early AD diagnosis. Conventional ML algorithms like support vector machines, decision trees, and K nearest neighbor have been extensively employed for AD diagnosis through relevant feature extraction from heterogeneous data sources. Conversely, deep learning techniques such as multilayer perceptron (MLP) and convolutional neural networks (CNNs) have demonstrated exceptional aptitude in autonomously uncovering intricate patterns and representations from unprocessed data like EEG data. The findings reveal that while traditional ML methods may perform adequately with limited data, deep learning techniques excel when ample data is available, showcasing their potential for early and precise AD diagnosis. In conclusion, this research paper contributes to the ongoing discourse surrounding the choice of appropriate methodologies for early Alzheimer’s disease diagnosis.

Electronic health records with decision support systems for sharper diagnoses: bibliometric analysis

Clinical decision support systems (CDSS) and electronic health records (EHRs) are computer systems designed to assist decision-makers in making optimal and streamlined judgments on disease diagnosis, treatment, patient care, and health institution management. This study conducted descriptive and bibliometric evaluations of CDSS integrated with EHRs studies published in journals included in the Scopus database from 2007 to 2024. During the initial phase, the publications were distributed based on their publication year, nation, institution, journal, and citation numbers, as part of a descriptive analysis. During the second stage, the articles were subjected to bibliometric analysis, which involved doing common keyword analyses. The research yielded 409 papers about CDSS and EHRs. The United States has been identified as the country with the highest number of studies on this issue. The journal with the highest number of citations observed was Studies in Health Technology and Informatics. Furthermore, the text showcases visual representations of co-citations and cooperation between authors from different institutions and countries. This study aims to present a systematic framework for examining CDSS with EHRs to improve diagnosis and offer a comprehensive viewpoint to researchers and specialists in the field.

Apport du scanner cardiaque dans la prise en charge des cardiopathies congénitales : expérience de l'institut de cardiologie d'Abidjan à propos de 27 cas

Introduction and objectivesIn contrast to developed countries, cardiac CT is not widely used in West Africa for the assessment of congenital heart disease, and has only recently been introduced in Côte d'Ivoire. The lack of data prompted this study, the aim of which was to describe our experience of the contribution of CT to the management of congenital heart disease in the Ivorian cardiology setting. Patients and methodThis was a prospective study which took place in the pediatric cardiology department over a period of 9 months (September 2022 to June 2023) which included all patients with congenital heart disease explored by echocardiography and cardiac scan. ResultsThe average age was 5.7 ± 4.7 years with extremes of 5 months and 16 years. We noted a female predominance with a sex ratio of 0.52. The main heart diseases were: tetralogy of Fallot (37.1%), followed by pulmonary atresia with open septum (18.52%). The assessment of the anatomy of the pulmonary arteries (81.48%), the search for aortopulmonary collaterals (59.63%) and finally the assessment of the anatomy of the aorta (18.52%) were the main indications. The practice of cardiac CT scanning has enabled better accuracy in the diagnosis and management of congenital heart disease in several cases: an interruption of the aortic arch was found in 1 case initially suspected of hypoplasia of the aortic arch, three cases of partial abnormal pulmonary venous return and one case of total abnormal pulmonary venous return. Also, agenesis of the left pulmonary artery with birth anomaly of the right coronary artery in the assessment of tetralogy of Fallot was found in 1 case. The CT scan made it possible to specify the exact topography of the aortopulmonary collaterals in four cases of pulmonary atresia with an open septum. ConclusionCT has enabled better visualization of the arterial and venous pulmonary tree, identification and exact localization of aorto-pulmonary collaterals, precise description of the three-dimensional anatomy of the coronary arteries, and better precision of congenital anomalies of the aorta.

MiR-26a is a Key Therapeutic Target with Enormous Potential in the Diagnosis and Prognosis of Human Disease.

MicroRNA-26a (miR-26a) belongs to small non-coding regulatory RNA molecules emerging as fundamental post-transcriptional regulators inhibiting gene expression that plays vital roles in various processes of human diseases such as depression, renal ischemia and reperfusion injury, liver injury and some refractory cancer. In this review, we expound on the results of studies about miR-26a with emphasis on its function in animal models or in vitro cell culture to simulate the most common human disease in the clinic. Furthermore, we also illustrate the underlying mechanisms of miR-26a in strengthening the antitumor activity of antineoplastic drugs. Importantly, dysregulation of miR-26a has been related to many chronic and malignant diseases, especially in neurological disorders in the brain such as depression and neurodegenerative diseases as well as cancers such as papillary thyroid carcinoma, hepatocellular carcinoma and so on. It follows that miR-26a has a strong possibility to be a potential therapeutic target for the treatment of neurological disorders and cancers. Although the research of miRNAs has made great progress in the last few decades, much is yet to be discovered, especially regarding their underlying mechanisms and roles in the complex diseases of humans. Consequently, miR-26a has been analyzed in chronic and malignant diseases, and we discuss the dysregulation of miR-26a and functional roles in the development and pathogenesis of these diseases, which is very helpful for understanding their mechanisms as new biomarkers for diagnosing and curing diseases in the near future.

Whole-exome-based single nucleotide variants and copy number analysis for prenatal diagnosis of compound heterozygosity of SMPD4.

Biallelic loss-of-function variants in SMPD4 cause a rare and severe neurodevelopmental disorder. These variants have been identified in a group of children with neurodevelopmental disorders with microcephaly, arthrogryposis, and structural brain anomalies. SMPD4 encodes a sphingomyelinase that hydrolyzes sphingomyelin into ceramide at neutral pH and can thereby affect membrane lipid homeostasis. SMPD4 localizes to the membranes of the endoplasmic reticulum and nuclear envelope and interacts with nuclear pore complexes. For the efficient prenatal diagnosis of rare and undiagnosed diseases, the parallel detection of copy number variants (CNVs) and single nucleotide variants using whole-exome analysis is required. A physical examination of the parents was performed. Karyotype and whole-exome analysis were performed for the fetus and the parents. A fetus with microcephaly and arthrogryposis; biallelic null variants (c.387-1G>A; Chr2[GRCh38]: g.130142742_130202459del) were detected by whole-exome sequencing (WES). We have reported for the first time the biallelic loss-of-function mutations in SMPD4 in patients born to unrelated parents in China. WES could replace chromosomal microarray analysis and copy number variation sequencing as a more cost-effective genetic test for detecting CNVs and diagnosing highly heterogeneous conditions.

Predictive models in Alzheimer's disease: an evaluation based on data mining techniques

The increasing prevalence of Alzheimer's disease in older adults has raised significant concern in recent years. Aware of this challenge, this research set out to develop predictive models that allow early identification of people at risk for Alzheimer's disease, considering several variables associated with the disease. To achieve this objective, data mining techniques were employed, specifically the decision tree algorithm, using the RapidMiner Studio tool. The sample explore modify model and assess (SEMMA) methodology was implemented systematically at each stage of model development, ensuring an orderly and structured approach. The results obtained revealed that 45.00% of people with dementia present characteristics that identify them as candidates for confirmation of a diagnosis of Alzheimer's disease. In contrast, 52.78% of those who do not have dementia show no danger of contracting the disease. In the conclusion of the research, it was noted that most patients diagnosed with Alzheimer's are older than 65 years, indicating that this stage of life tends to trigger brain changes associated with the disease. This finding underscores the importance of considering age as a key factor in the early identification of the disease.

Analysis of potential Circular RNAs in regulating imatinib resistance of Gastrointestinal stromal tumor.

Recent studies have found that circular RNA is an abundant RNA species that belongs to part of the competing endogenous RNA network(ceRNA), which was proven to play an important role in the development, diagnosis and progress of diseases. However, the function of circRNAs in imatinib resistance in Gastrointestinal stromal tumor (GIST) are poorly understood so for. The present study aimed to screen and predict the potential circRNAs in imatinib resistance of GIST using microarray analysis. We determined the expression of circular RNAs in paired normal gastric tissues(N), primary GIST (gastrointestinal stromal tumor) tissues (YC) and imatinib mesylate secondary resistance GIST tissues(C) with microarray and predicted 8677 dysregulated circular RNAs. Compared with the YC group, we identified 15 circRNAs that were up-regulated and 8 circRNAs that were down-regulated in the C group. Gene ontology (GO)and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these host linear transcripts that differentially express circular RNAs are involved in many key biological pathways, predicting the potential tumor-genesis and drug resistance mechanismrelated to HIF-1 pathway, later we draw the cirRNA-miRNA-mRNA network involved in the HIF-1 pathway and found several dysregulated circRNAs and the relationship between circRNA-miRNAs-mRNA, such as circRNA_06551, circRNA_14668, circRNA_04497, circRNA_08683, circRNA_09923(Green, down-regulation) and circRNA_23636, circRNA_15734(Red, up-regulation). Taken together, we identified a panel of dysregulated circRNAs that may be potential biomarkers even therapy relevant to the GIST, especially imatinib secondary resistance GIST.

Automated DeepLabV3+ based model for left ventricle segmentation on short-axis late gadolinium enhancement-magnetic cardiac resonance imaging images

Accurate segmentation of myocardial scar tissue on late gadolinium enhancement-magnetic cardiac resonance imaging (LGE-CMR) is exceptionally vital for clinical applications, enabling precise diagnosis and effective treatment of various cardiac diseases, such as myocardial infarction and cardiomyopathies. However, the ventricle (LV) variations in the size and shape, artifacts, and image resolution of LGE-CMR has made automatic segmentation of myocardial scar tissue more challenging. While many existing approaches delineate the LV myocardium region using multi-modal segmentation, these models may be computationally complex and suffer from misalignment. Therefore, this study proposed an automatic dual-stage DeepLabV3+ based approach tailored for myocardial scar segmentation on short-axis LGE-MRI exclusively. To segment myocardial scar tissue, the second stage employs the segmented LV chamber from the previous stage. The encoder part of the framework utilizes a MobileNetV2 and ResNet50 backbone for the first and second segmentation, respectively, aiming for optimal resolution of feature maps. Both stages tailor an improved Atrous Spatial Pyramid Pooling module in the DeepLabV3+ model with fine-tuned dilated atrous rates to effectively extract the LV chamber and myocardial scar from the complex LGE-MRI background. Based on the results, the proposed dual-stage network recorded an outstanding segmentation performance, with mean Dice score of 96.02% for LV chamber segmentation and 68.01% for scar tissue extraction.

Trident anchoring-lipid droplets strategy for fluorescence/super-resolution/lifetime imaging and diagnosis of fatty liver in vivo

Lipid droplets (LDs) are crucial dynamic organelles within cells that play an indispensable role in cellular energy supply, membrane formation, and signal transduction. Therefore, detecting and tracking LDs with high selectivity and resolution is particularly important. In this study, we describe a “trident” anchoring strategy (based on polarity, lipophilicity, and viscosity sensitivity) to achieve highly specific and dynamic imaging of LDs. A series of carbazole-substituted fluorescent protein (Car-FP) mimics conjugated with different electron-nature substituents (A–H) were synthesized and characterized. Spectroscopic properties indicate that the electron-donating triphenylamine (TPA)-conjugated H exhibits high sensitivity to polarity, viscosity response, and lipophilicity. Co-localization experiments in cells also demonstrate that H selectively localizes at LDs in living cells, displaying a bright yellow fluorescence signal (λex = 488 nm; λem = 577 nm) with a larger Stokes shift (89 nm) than the commercial LD dye bodipy 493/503 (10 nm). Super-resolution fluorescence imaging (SIM) and lifetime imaging (FLIM) are employed to visualize the distribution and aggregation in cell models of nonalcoholic fatty liver disease as well as zebrafish and mice models, demonstrating the potential applications of H in LD biology research and diagnosis of related diseases.

Advancements in diabetic kidney disease management: integrating innovative therapies and targeted drug development.

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease and affects approximately 40% of individuals with diabetes . Cases of DKD continue to rise globally as the prevalence of diabetes mellitus increases, with an estimated 415 million people living with diabetes in 2015 and a projected 642 million by 2040. DKD is associated with significant morbidity and mortality, representing 34% and 36% of all chronic kidney disease deaths in men and women, respectively. Common comorbidities including hypertension and ageing-related nephron loss further complicate disease diagnosis and progression. The progression of DKD involves several mechanisms including glomerular endothelial cell dysfunction, inflammation, and fibrosis. Targeting these mechanisms has formed the basis of several therapeutic agents. Renin-angiotensin-aldosterone system (RAAS) blockers, specifically angiotensin receptor blockers (ARBs), demonstrate significant reductions in macroalbuminuria. Sodium-glucose transporter type 2 (SGLT-2) inhibitors demonstrate kidney protection independent of diabetes control while also decreasing the incidence of cardiovascular events. Emerging agents including glucagon-like peptide 1 (GLP-1) agonists, anti-inflammatory agents like bardoxolone, and mineralocorticoid receptor antagonists show promise in mitigating DKD progression. Many novel therapies including monoclonal antibodies CSL346, lixudebart, and tozorakimab; mesenchymal stem/stromal cell infusion; and cannabinoid-1 receptor inverse agonism via INV-202 are currently in clinical trials and present opportunities for further drug development.

Novel picture fuzzy distance measure [formula omitted] and its application in medical diagnosis

In this research work, we introduced novel distance measure in picture fuzzy sets and prove all the triangle inequality associated with it. There is an introduction to the use of the distance measure in medical diagnostics. In addition to this, we have shown a comparison between two distance functions ρ and ρ∗ in this research work. Our observation indicates that the novel fuzzy distance measure ρ∗ exhibits greater efficacy in the context of disease diagnosis compared to the conventional measure ρ.

Multi-granularity tooth analysis via YOLO-based object detection models for effective tooth detection and classification

Accurate detection and classification of teeth is the first step in dental disease diagnosis. However, the same class of tooth exhibits significant variations in surface appearance. Moreover, the complex geometrical structure poses challenges in learning discriminative features among the different tooth classes. Due to these complex features, tooth classification is one of the challenging research domains in deep learning. To address the aforementioned issues, the presented study proposes discriminative local feature extraction at different granular levels using YOLO models. However, this necessitates a granular intra-oral image dataset. To facilitate this requirement, a dataset at three granular levels (two, four, and seven teeth classes) is developed. YOLOv5, YOLOv6, and YOLOv7 models were trained using 2,790 images. The results indicate superior performance of YOLOv6 for two-class classification problems. The model generated a mean average precision (mAP) value of 94%. However, as the granularity level is increased, the performance of YOLO models decreases. For, four and seven-class classification problems, the highest mAP value of 87% and 79% was achieved by YOLOv5 respectively. The results indicate that different levels of granularity play an important role in tooth detection and classification. The YOLO’s performance gradually decreased as the granularity decreased especially at the finest granular level.

Impact of British Society of Gastroenterology non-biopsy guidance for coeliac disease diagnosis

Impact of British Society of Gastroenterology non-biopsy guidance for coeliac disease diagnosis

Comparative Analysis of VGG16, RESNET50, AND CNN Models for Lung Disease Prediction: A Deep Learning Approach

Abstract: Utilizing deep learning methods in medical image analysis has shown promise in enhancing disease detection and diagnosis. In this study, we conducted a detailed comparative analysis of threewidely recognized convolutional neural network (CNN) architectures: VGG16, ResNet50, and a custom CNN model tailored for pneumonia and COVID-19 detection from chest X-ray images. Leveraging transfer learning techniques and meticulously curated datasets, we evaluated the models' performance in accurately identifying respiratory diseases. Our investigation utilized two publicly available datasets, the ChestX-ray14 dataset and the NIH Chest X-ray Dataset, both annotated for pneumonia and COVID-19. Prior to model training, we conducted thorough preprocessing to ensure optimal data quality and consistency. Through rigorous experimentation, we assessed the models' accuracy, sensitivity, and specificity in disease detection. The results revealed nuanced differences in model performance across disease categories. While VGG16 demonstrated robust accuracy in pneumonia detection, ResNet50 exhibited enhanced sensitivity and specificity in identifying COVID-19 cases. Our custom CNN model, leveraging insights from both architectures, showcased competitive performance, emphasizing the importance of tailored model design for optimal diagnostic outcomes. Through comprehensive analysis and discussion, we elucidated the strengths and limitations of each model, considering factors such as computational efficiency, interpretability, and generalizability. Our findings underscore the potential of deep learningbased diagnostic tools in supporting healthcare professionals in timely and accurate disease diagnosis

Leaf Disease Detection Using Convolutional Neural Network

Leaf Disease Detection Using Convolutional Neural Network