Determination of the Early Time of Death by Computerized Image Analysis of DNA Degradation: Which Is the Best Quantitative Indicator of DNA Degradation?

This study evaluated the correlation between DNA degradation of the splenic lymphocytes and the early time of death, examined the early time of death by computerized image analysis technique （CIAT） and identified the best parameter that quantitatively reflects the DNA degradation. The spleen tissues from 34 SD rats were collected, subjected to cell smearing every 2 h within the first 36 h after death, stained by Feulgen-Van＇s staining, three indices reflecting DNA content in splenic lymphocytes, including integral optical density （IOD）, average optical density （AOD）, average gray scale （AG） were measured by the image analysis. Our results showed that IOD and AOD decreased and AG increased over time within the first 36 h. A stepwise linear regression analysis showed that only AG was fitted. A correlation between the postmortem interval （PMI） and AG was identified and the corresponding regression equation was obtained. Our study suggests that CIAT is a useful and promising tool for the estimation of early PMI with good objectivity and reproducibility, and AG is a more effective and better quantitative indicator for the estimation of PMI within the first 36 h after death in rats.

Computer Image Analysis as a Tool for Microbial Viability Assessment: Examples of Use and Prospects

Application of the computer image analysis for improving microbial viability assessment by plate count and fluorescence microscopy was investigated. Yeast cells were used as a model microorganism. The application of the improved methods for the viability assessment of yeast cells after preservation by freezing and freeze-drying was demonstrated.

Assessment and Visualization of Ki67 Heterogeneity in Breast Cancers through Digital Image Analysis

The Ki67 index (KI) is a standard clinical marker for tumor proliferation;however, its application is hindered by intratumoral heterogeneity. In this study, we used digital image analysis to comprehensively analyze Ki67 heterogeneity and distribution patterns in breast carcinoma. Using Smart Pathology software, we digitized and analyzed 42 excised breast carcinoma Ki67 slides. Boxplots, histograms, and heat maps were generated to illustrate the KI distribution. We found that 30% of cases (13/42) exhibited discrepancies between global and hotspot KI when using a 14% KI threshold for classification. Patients with higher global or hotspot KI values displayed greater heterogenicity. Ki67 distribution patterns were categorized as randomly distributed (52%, 22/42), peripheral (43%, 18/42), and centered (5%, 2/42). Our sampling simulator indicated analyzing more than 10 high-power fields was typically required to accurately estimate global KI, with sampling size being correlated with heterogeneity. In conclusion, using digital image analysis in whole-slide images allows for comprehensive Ki67 profile assessment, shedding light on heterogeneity and distribution patterns. This spatial information can facilitate KI surveys of breast cancer and other malignancies.

A computer-based image analysis for tear ferning featuring

The present work focuses on the development of a novel computer-based approach for tear ferning(TF)featuring.The original TF images of the recently developedfive-point grading scale have been used to assign a grade for any TF image automatically.A vector characteristic(VC)representing each grade was built using the reference images.A weighted combination between features selected from textures analysis using gray level co-occurrence matrix(GLCM),power spectrum(PS)analysis and linear specificity of the image were used to build the VC of each grade.A total of 14 features from texture analysis were used.PS at di®erent frequency points and number of line segments in each image were also used.Five features from GLCM have shown significant di®erences between the recently developed grading scale images which are:angular second moment at 0
and 45
,contrast,and correlation at 0
and 45
;thesefive features were all included in the characteristic vector.Three specific power frequencies were used in the VC because of the discrimination power.Number of line segments was also chosen because of dissimilarities between images.A VC for each grade of TF reference images was constructed and was found to be significantly different from each other's.This is a basic and fundamental step toward an automatic grading for computer-based diagnosis for dry eye.

Computer-aided clinical image analysis as a predictor of sentinel lymph node positivity in cutaneous melanoma

BACKGROUND Delays in sentinel lymph node(SLN)biopsy may affect the positivity of non-SLNs.For these reasons,effort is being directed at obtaining reliable information regarding SLN positivity prior to surgical excision.However,the existing tools,e.g.,dermoscopy,do not recognize statistically significant predictive criteria for SLN positivity in melanomas.AIM To investigate the possible association of computer-assisted objectively obtained color,color texture,sharpness and geometry variables with SLN positivity.METHODS We retrospectively reviewed and analyzed the computerized medical records of all patients diagnosed with cutaneous melanoma in a tertiary hospital in Germany during a 3-year period.The study included patients with histologically confirmed melanomas with Breslow>0.75 mm who underwent lesion excision and SLN biopsy during the study period and who had clinical images shot with a digital camera and a handheld ruler aligned beside the lesion.RESULTS Ninety-nine patients with an equal number of lesions met the inclusion criteria and were included in the analysis.Overall mean(±standard deviation)age was 66(15)years.The study group consisted of 20 patients with tumor-positive SLN(SLN+)biopsy,who were compared to 79 patients with tumor-negative SLN biopsy specimen(control group).The two groups differed significantly in terms of age(61 years vs 68 years)and histological subtype,with the SLN+patients being younger and presenting more often with nodular or secondary nodular tumors(P<0.05).The study group patients showed significantly higher eccentricity(i.e.distance between color and geometrical midpoint)as well as higher sharpness(i.e.these lesions were more discrete from the surrounding normal skin,P<0.05).Regarding color variables,SLN+patients demonstrated higher range in all four color intensities(gray,red,green,blue)and significantly higher skewness in three color intensities(gray,red,blue),P<0.05.Color texture variables,i.e.lacunarity,were comparable in both groups.CONCLUSION SLN+patients demonstrated significantly higher eccentricity,higher sharpness,higher range in all four color intensities(gray,red,green,blue)and significantly higher skewness in three color intensities(gray,red,blue).Further prospective studies are needed to better understand the effectiveness of clinical image processing in SLN+melanoma patients.

Diagnostic efficacy of virtual organ computer-assisted analysis in measuring the volume ratio of subchorionic hematoma with serum progesterone

BACKGROUND Subchorionic hematoma(SCH)is a common complication in early pregnancy characterized by the accumulation of blood between the uterine wall and the chorionic membrane.SCH can lead to adverse pregnancy outcomes such as miscarriage,preterm birth,and other complications.Early detection and accurate assessment of SCH are crucial for appropriate management and improved pregnancy outcomes.AIM To evaluate the diagnostic efficacy of virtual organ computer-assisted analysis(VOCAL)in measuring the volume ratio of SCH to gestational sac(GS)combined with serum progesterone on early pregnancy outcomes in patients with SCH.METHODS A total of 153 patients with SCH in their first-trimester pregnancies between 6 and 11 wk were enrolled.All patients were followed up until a gestational age of 20 wk.The parameters of transvaginal two-dimensional ultrasound,including the circumference of SCH(Cs),surface area of SCH(Ss),circumference of GS(Cg),and surface area of GS(Sg),and the parameters of VOCAL with transvaginal three-dimensional ultrasound,including the three-dimensional volume of SCH(3DVs)and GS(3DVg),were recorded.The size of the SCH and its ratio to the GS size(Cs/Cg,Ss/Sg,3DVs/3DVg)were recorded and compared.RESULTS Compared with those in the normal pregnancy group,the adverse pregnancy group had higher Cs/Cg,Ss/Sg,and 3DVs/3DVg ratios(P<0.05).When 3DVs/3DVg was 0.220,the highest predictive performance predicted adverse pregnancy outcomes,resulting in an AUC of 0.767,and the sensitivity,specificity were 70.2%,75%respectively.VOCAL measuring 3DVs/3DVg combined with serum progesterone gave a diagnostic AUC of 0.824 for early pregnancy outcome in SCH patients,with a high sensitivity of 82.1%and a specificity of 72.1%,which showed a significant difference between AUC.CONCLUSION VOCAL-measured 3DVs/3DVg effectively quantifies the severity of SCH,while combined serum progesterone better predicts adverse pregnancy outcomes.

Artificial Intelligence and Computer Vision during Surgery: Discussing Laparoscopic Images with ChatGPT4—Preliminary Results

Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce. Aim: To test a novel AI commercially available tool for image analysis on a series of laparoscopic scenes. Methods: The research tools included OPENAI CHATGPT 4.0 with its corresponding image recognition plugin which was fed with a list of 100 laparoscopic selected snapshots from common surgical procedures. In order to score reliability of received responses from image-recognition bot, two corresponding scales were developed ranging from 0 - 5. The set of images was divided into two groups: unlabeled (Group A) and labeled (Group B), and according to the type of surgical procedure or image resolution. Results: AI was able to recognize correctly the context of surgical-related images in 97% of its reports. For the labeled surgical pictures, the image-processing bot scored 3.95/5 (79%), whilst for the unlabeled, it scored 2.905/5 (58.1%). Phases of the procedure were commented in detail, after all successful interpretations. With rates 4 - 5/5, the chatbot was able to talk in detail about the indications, contraindications, stages, instrumentation, complications and outcome rates of the operation discussed. Conclusion: Interaction between surgeon and chatbot appears to be an interesting frontend for further research by clinicians in parallel with evolution of its complex underlying infrastructure. In this early phase of using artificial intelligence for image recognition in surgery, no safe conclusions can be drawn by small cohorts with commercially available software. Further development of medically-oriented AI software and clinical world awareness are expected to bring fruitful information on the topic in the years to come.

Diagnostic Accuracy of Computerized Bowel Sound Analysis with Non-Invasive Devices for Irritable Bowel Syndrome:A Systematic Review and Meta-Analysis

Objective To assess the diagnostic accuracy of bowel sound analysis for irritable bowel syndrome(IBS)with a systematic review and meta-analysis.Methods We searched MEDLINE,Embase,the Cochrane Library,Web of Science,and IEEE Xplore databases until September 2023.Cross-sectional and case-control studies on diagnostic accuracy of bowel sound analysis for IBS were identified.We estimated the pooled sensitivity,specificity,positive likelihood ratio,negative likeli-hood ratio,and diagnostic odds ratio with a 95% confidence interval(CI),and plotted a summary receiver operat-ing characteristic curve and evaluated the area under the curve.Results Four studies were included.The pooled diagnostic sensitivity,specificity,positive likelihood ratio,nega-tive likelihood ratio,and diagnostic odds ratio were 0.94(95%CI,0.87‒0.97),0.89(95%CI,0.81‒0.94),8.43(95%CI,4.81‒14.78),0.07(95%CI,0.03‒0.15),and 118.86(95%CI,44.18‒319.75),respectively,with an area under the curve of 0.97(95%CI,0.95‒0.98).Conclusions Computerized bowel sound analysis is a promising tool for IBS.However,limited high-quality data make the results'validity and applicability questionable.There is a need for more diagnostic test accuracy studies and better wearable devices for monitoring and analysis of IBS.

Optimal Classification of Minerals by Microscopic Image Analysis Based on Seven-State “Deep Learning” Combined with Optimizers

The development of artificial intelligence (AI), particularly deep learning, has made it possible to accelerate and improve the processing of data collected in different fields (commerce, medicine, surveillance or security, agriculture, etc.). Most related works use open source consistent image databases. This is the case for ImageNet reference data such as coco data, IP102, CIFAR-10, STL-10 and many others with variability representatives. The consistency of its images contributes to the spectacular results observed in its fields with deep learning. The application of deep learning which is making its debut in geology does not, to our knowledge, include a database of microscopic images of thin sections of open source rock minerals. In this paper, we evaluate three optimizers under the AlexNet architecture to check whether our acquired mineral images have object features or patterns that are clear and distinct to be extracted by a neural network. These are thin sections of magmatic rocks (biotite and 2-mica granite, granodiorite, simple granite, dolerite, charnokite and gabbros, etc.) which served as support. We use two hyper-parameters: the number of epochs to perform complete rounds on the entire data set and the “learning rate” to indicate how quickly the weights in the network will be modified during optimization. Using Transfer Learning, the three (3) optimizers all based on the gradient descent methods of Stochastic Momentum Gradient Descent (sgdm), Root Mean Square Propagation (RMSprop) algorithm and Adaptive Estimation of moment (Adam) achieved better performance. The recorded results indicate that the Momentum optimizer achieved the best scores respectively of 96.2% with a learning step set to 10−3 for a fixed choice of 350 epochs during this variation and 96, 7% over 300 epochs for the same value of the learning step. This performance is expected to provide excellent insight into image quality for future studies. Then they participate in the development of an intelligent system for the identification and classification of minerals, seven (7) in total (quartz, biotite, amphibole, plagioclase, feldspar, muscovite, pyroxene) and rocks.

Automated deep learning system for power line inspection image analysis and processing: architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .

Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System

Medical image analysis is an active research topic,with thousands of studies published in the past few years.Transfer learning(TL)including convolutional neural networks(CNNs)focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance.It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time.This study develops an EnhancedTunicate SwarmOptimization withTransfer Learning EnabledMedical Image Analysis System(ETSOTL-MIAS).The goal of the ETSOTL-MIAS technique lies in the identification and classification of diseases through medical imaging.The ETSOTL-MIAS technique involves the Chan Vese segmentation technique to identify the affected regions in the medical image.For feature extraction purposes,the ETSOTL-MIAS technique designs a modified DarkNet-53 model.To avoid the manual hyperparameter adjustment process,the ETSOTLMIAS technique exploits the ETSO algorithm,showing the novelty of the work.Finally,the classification of medical images takes place by random forest(RF)classifier.The performance validation of the ETSOTL-MIAS technique is tested on a benchmark medical image database.The extensive experimental analysis showed the promising performance of the ETSOTL-MIAS technique under different measures.

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

Automatic Computer Analysis of Digital Images of Triple-Antibody-Stained Prostate Biopsies

Background: Worldwide, prostatic adenocarcinoma is the most common tumour type among men. Aim: The aim of the present investigation was to develop a computer program to identify normal prostate biopsies and distinguish them from biopsies showing premalignant alterations (LGPIN, HGPIN) and adenocarcinoma. Method: Prostate biopsies (n = 2094) taken from 191 consecutive men during 2016 were stained with triple immunehistochemisty (antibodies to AMACRA, p63 and CK 5). Digital images of the biopsies were obtained with a scanning microscope and used to develop an automatic computer program (CelldaTM), intended to identify the morphological alterations. Visual microscopic finding was used as a reference. Result: Of the 191 men, 121 (63.4%) were diagnosed as having prostate adenocarcinoma and 70 (36.6%) as having no malignancy on the basis of the visual microscopy. In comparison, computer analysis identified 134 (70.2%) men with malignant disease and 57 (29.8%) with non-malignant disease after exclusion of artifacts, which constituted 10.4% of areas (indicated as malignant disease). Discrepant results were recorded in 15 (7.9%) men, and in 14 of these cases, HGPIN and areas suggestive of early invasion were common. Thus, it was uncertain whether these cases should be regarded as malignant or not. The agreement between the visual examination and the computer analysis was 92.1% (kappa value 0.823, sensitivity 99.2 and specificity was 0.80). Conclusion: It seems that computer analysis could serve as an adjunct to simplify and shorten the diagnostic procedure, first of all by ensuring that normal prostate biopsies are sorted out from those sent for visual microscopic evaluation.

Multidetector computer tomography and magnetic resonance imaging of double superior mesenteric veins:A case report

BACKGROUND This study aimed to describe the findings of double superior mesenteric veins(SMVs),a rare anatomical variation,on multidetector computer tomography(MDCT)and magnetic resonance imaging(MRI)images.CASE SUMMARY We describe the case of a 34-year-old male,who underwent both MDC and MRI examinations of the upper abdomen because of liver cirrhosis.MDCT and MRI angiography images of the upper abdomen revealed an anatomic variation of the superior mesenteric vein(SMV),the double SMVs.CONCLUSION The double SMVs are a congenital abnormality without potential clinical manifestation.Physicians need to be aware of this anatomical variation during abdominal surgery to avoid iatrogenic injury.

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

Risk identification and safety assessment of human-computer interaction in integrated avionics based on STAMP

To solve the problem of risk identification and quantitative assessment for human-computer interaction(HCI)in complex avionics systems,an HCI safety analysis framework based on system-theoretical process analysis(STPA)and cognitive reliability and error analysis method(CREAM)is proposed.STPACREAM can identify unsafe control actions and find the causal path during the interaction of avionics systems and pilot with the help of formal verification tools automatically.The common performance conditions(CPC)of avionics systems in the aviation environment is established and a quantitative analysis of human failure is carried out.Taking the head-up display(HUD)system interaction process as an example,a case analysis is carried out,the layered safety control structure and formal model of the HUD interaction process are established.For the interactive behavior“Pilots approaching with HUD”,four unsafe control actions and35 causal scenarios are identified and the impact of common performance conditions at different levels on the pilot decision model are analyzed.The results show that HUD's HCI level gradually improves as the scores of CPC increase,and the quality of crew member cooperation and time sufficiency of the task is the key to its HCI.Through case analysis,it is shown that STPACREAM can quantitatively assess the hazards in HCI and identify the key factors that impact safety.

Computer Vision-Based Human Body Posture Correction System

With the development of technology and the progress of life,more and more people,regardless of entertainment,learning,or work,cannot do without computer desks and cannot put down their mobile phones.Due to prolonged sitting and often neglecting the importance of posture,incorrect posture can often lead to health problems such as hunchback,lumbar muscle strain,and shoulder and neck pain over time.To address this issue,we designed a computer vision-based human body posture detection system.The system utilizes YOLOv8 technology to accurately locate key points of the human body skeleton,and then analyzes the coordinate positions and depth information of these key points to establish a criterion for distinguishing different postures.With the assistance of an SVM classifier,the system achieves an average recognition rate of 95%.Finally,we successfully deployed the posture detection system on Raspberry Pi hardware and conducted extensive testing.The test results demonstrate that the system can effectively detect various postures and provide real-time reminders to users to correct poor posture,demonstrating good practicality and stability.

Advancing spinal cord injury research with optical clearing,light sheet microscopy,and artificial intelligence-based image analysis

From the days of Ramon y Cajal's first sketches,neuroscientists have recognized the importance of visualizing the complex architecture of the central nervous system.In the past century,we have come to appreciate how the rich structural and functional complementarity of axons and cell types in the spinal cord make it uniquely suited for information transfer between the periphery and the brain.

Research on Automatic Elimination of Laptop Computer in Security CT Images Based on Projection Algorithm and YOLOv7-Seg

In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.

An Adaptive Edge Detection Algorithm for Weed Image Analysis

Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops.Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy.The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields.Some weed methods have been proposed for these fields;however,these algorithms still have challenges as they were implemented against controlled environments.Therefore,in this paper,a weed image analysis approach has been proposed for the system of weed classification.In this system,for preprocessing,a Homomorphic filter is exploited to diminish the environmental factors.While,for feature extraction,an adaptive feature extraction method is proposed that exploited edge detection.The proposed technique estimates the directions of the edges while accounting for non-maximum suppression.This method has several benefits,including its ease of use and ability to extend to other types of features.Typically,low-level details in the formof features are extracted to identify weeds,and additional techniques for detecting cultured weeds are utilized if necessary.In the processing of weed images,certain edges may be verified as a footstep function,and our technique may outperform other operators such as gradient operators.The relevant details are extracted to generate a feature vector that is further given to a classifier for weed identification.Finally,the features have been used in logistic regression for weed classification.The model was assessed against logistic regression that accurately identified different kinds of weed images in naturalistic domains.The proposed approach attained weighted average recognition of 98.5%against the weed images dataset.Hence,it is assumed that the proposed approach might help in the weed classification system to accurately identify narrow and broad weeds taken captured in real environments.