Convolutional Neural Network-based Model Research Articles

Automatic Root Length Estimation from Images Acquired InSitu without Segmentation.

Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the insitu root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination (R2), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models' performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.

MeFunX: A novel meta-learning-based deep learning architecture to detect fungal infection directly from microscopic images

Fungal infections are a growing threat to human health. They can lead to a range of health problems and can be life-threatening. There are many impediments to the traditional diagnosis of fungal infections, such as a diminishing number of clinical mycologists, expensive procedures, high time consumption, and requirements for sensitivity and specificity. Therefore, early diagnosis of fungal infection is critical to effective treatment. In this paper, a novel meta-learning-based deep learning architecture, termed MeFunX, is proposed for the early detection of fungal infections from microscopic images. MeFunX architecture consisted of two convolutional neural network-based models as base learners and XGBoost as the meta-learner. To assess the proposed approach, standard metrics namely, accuracy, precision, recall and f1-score, were used. The fungal disease identification performance of MeFunX was compared with state-of-the-art architectures like VGG16, InceptionV3, ResNet, AlexNet, DenseNet, and EfficientNet. In addition, MeFunX was also benchmarked against its base learners and other meta-learning model with EfficientNet and ResNet as the base learners to demonstrate the effectiveness of the meta-learning architecture. Rigorous experimentation clearly signifies the superior performance of MeFunX, which achieved an overall accuracy of 92.49% for the early diagnoses of fungal infections in microscopic images.

1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing

Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning techniques to spectrum sensing has attracted considerable interest in the literature. In this contribution, we study cooperative spectrum sensing in a cognitive radio network where multiple secondary users cooperate to detect a primary user. We introduce multiple cooperative spectrum sensing schemes based on a deep neural network, which incorporate a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity patterns of the primary user. The scenario of an imperfect transmission channel is considered for service messages to demonstrate the robustness of the proposed model. The performance of the proposed methods is evaluated with the receiver operating characteristic curve, the probability of detection for various SNR levels and the computational time. The simulation results confirm the effectiveness of the bidirectional long short-term memory-based method, surpassing the performance of the other proposed schemes and the current state-of-the-art methods in terms of detection probability, while ensuring a reasonable online detection time.

Identifying the probability of genetic mutations in lung cancer using predictive and prognostic biomarkers from histopathological images

Background: Lung cancer is the highest deadliest disease and second largest disease being diagnosed worldwide. In the age of precision medicine, determining a patient’s genetic status is critical. Finding the percentage of gene mutation of a particular biomarker will help in targeted therapy of a patient at an early stage. Objective: Histopathology images are larger in size which needs to be converted into smaller tiles for the computational purpose. Deep Learning Techniques could be applied on this huge number of histopathological images to derive the probability of gene mutation occurrence in predictive and prognostic biomarkers of lung cancer. Methods: In this work, a deep learning convolutional neural network (CNN) model (InceptionV3) is trained on histopathology images obtained from The Cancer Genome Atlas (TCGA) to accurately predict the mutated genes in lung adenocarcinoma. The convolutional neural network-based model predicts 10 major genetic mutations percentage, i.e., EGFR, FAT1, FAT4, KEAP1, KRAS, LRP1B, NF1, SETBP1, STK11, TP53. Results: InceptionV3 predicted the probability of gene mutation from the histopathology images and categorized the genes as predictive and prognostic. InceptionV3 yielded an accuracy of 82.36% and cross entropy of 37.62%. Conclusion: InceptionV3 was trained on histopathology images to predict gene mutations with an accuracy of 82%. Prediction of gene mutations with different CNN models like AlexNet and ResNet can be explored further.

CNN with New Spatial Pyramid Pooling and Advanced Filter-Based Techniques: Revolutionizing Traffic Monitoring via Aerial Images

The escalation in vehicular traffic, in conjunction with global population growth, has precipitated heightened road congestion, augmented air pollution, and a rise in vehicular accidents. Over the past decade, the global vehicular count has witnessed a substantial surge. In this context, traffic monitoring emerges as a paramount challenge, especially within developing nations. This research introduces an innovative system for vehicle detection and categorization aimed at intelligent traffic monitoring. The system utilizes a convolutional neural network-based U-Net model for the segmentation of aerial images. After segmentation, the outputs are further analyzed for vehicle identification. This vehicle detection utilizes an advanced spatial pyramid pooling (ASPP) mechanism which refines the spatial partitions of the image and captures intricate details, enhancing the accuracy and precision of the detection process. Detected vehicles are then categorized into distinct subcategories. For the effective management and control of high-density traffic flow, the extended Kalman filter (EKF) technique is employed, thereby reducing the reliance on human oversight. In experimental evaluations, our proposed model exhibits exemplary vehicle detection capabilities across the German Aerospace Center (DLR3K) and the Vehicle Detection in Aerial Imagery (VEDAI) datasets. Potential applications of the system encompass vehicle identification in traffic streams, traffic congestion assessment, intersection traffic density analysis, differentiation of vehicle types, and pedestrian pathway determination.

Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system.

The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico model of neurodegeneration of the visual system by equipping it with a mechanism to simulate neuroplasticity. Therefore, deep convolutional networks of multiple sizes were trained for object recognition tasks and progressively lesioned to simulate neurodegeneration of the visual cortex. More specifically, the injured parts of the network remained injured while we investigated how the added retraining steps were able to recover some of the model's object recognition baseline performance. The results showed with retraining, model object recognition abilities are subject to a smoother and more gradual decline with increasing injury levels than without retraining and, therefore, more similar to the longitudinal cognition impairments of patients diagnosed with Alzheimer's disease (AD). Moreover, with retraining, the injured model exhibits internal activation patterns similar to those of the healthy baseline model when compared to the injured model without retraining. Furthermore, we conducted this analysis on a network that had been extensively pruned, resulting in an optimized number of parameters or synapses. Our findings show that this network exhibited remarkably similar capability to recover task performance with decreasingly viable pathways through the network. In conclusion, adding a retraining step to the in-silico setup that simulates neuroplasticity improves the model's biological feasibility considerably and could prove valuable to test different rehabilitation approaches in-silico.

Research and Evaluation of the Diagnostic Precision of an Artificial Intelligence Algorithm Using Ultrasound Images for Early Diagnosis of Arthropathy in People with Hemophilia

Background: Frequent joint bleeding in people with hemophilia can cause synovitis and progressive joint damage, leading to reduced quality of life over the long term. Despite widespread use of prophylactic treatment, frequent joint bleeding is still prevalent. Early diagnosis of symptomatic/asymptomatic hemarthrosis and synovitis is required through regular assessment to ensure appropriate therapeutic intervention. Ultrasonography is a convenient diagnostic tool, but accurate diagnosis of hemarthrosis and synovitis requires specialist skills and experience, and so its implementation in daily clinical practice is not currently widespread. By constructing an artificial intelligence (AI) algorithm to support diagnosis of hemarthrosis and synovitis using ultrasonography, increased uptake of joint ultrasonography is expected. This study researches and evaluates an AI algorithm for detecting the presence or absence of hemarthrosis and synovitis using ultrasound images obtained in people with hemophilia. Methods: This study evaluated the diagnostic precision of AI model-based diagnoses in people with congenital hemophilia A or B. Ankle, elbow, and knee joint ultrasound images obtained at study sites between January 1, 2010 and March 31, 2022 were used to train and test six AI models for each joint to estimate the presence or absence of hemarthrosis and synovitis. The ultrasound images were stratified by presence or absence of hemarthrosis and synovitis, as determined by an Executive Committee consisting of a physician and two orthopedists who are experts in hemophilia care. Images were then randomly divided, with 70% used to train the AI models and 30% used to test their diagnostic precision. The AI algorithm used various image pre-processing methods and the convolutional neural network-based AI model. The primary endpoint was the area under the receiver operating characteristic curve (AUC) for the diagnostic precision of the AI model to diagnose hemarthrosis and synovitis. The other endpoints were examined using parameters such as the rate of accuracy, precision, recall, and specificity. In addition, to evaluate whether the lesion area was detected correctly or not by visualization techniques, we displayed the lesion area with region of interest and expressed the discrimination basis using a heat map ( Figure 1). Results: A total of 5649 images were collected from five study sites. Following assessment by the Study Executive Committee,3435 images were used in this analysis. The AUC for hemarthrosis detection for the ankle, elbow, and knee joints was 0.87, 0.91, and 0.91, respectively, while for synovitis detection it was 0.90, 0.97, and 0.91, respectively ( Table 1). When evaluated in subgroups according to age, AUCs for hemarthrosis detection for at the ankle, elbow, and knee joints, respectively, were: 0.85, 0.94, and 0.98 at 10-19 years old; 0.82, 0.91, and 0.84 at 20-29 years old; 0.91, 0.90, and 0.89 at 30-39 years old; 0.90, 0.93, and 0.93 at 40-49 years old; and 0.91, 0.93, and 0.95 at 50-59 years old. AUCs for synovitis detection for the ankle, elbow, and knee joints, respectively, for different age groups were 0.95, 0.92, and 0.94 at 10-19 years old; 0.89, 0.97, and 0.95 at 20-29 years old; 0.94, 1.00, and 0.91 at 30-39 years old; 0.84, 0.96, and 0.91 at 40-49 years old; and 0.90, 0.98, and 0.91 at 50-59 years old. The analysis of the hemarthrosis and synovitis algorithms by joint site indicated high accuracy, precision, recall, and specificity ( Table 1). Conclusions: Although the AI model produced high AUC values for detecting hemarthrosis and synovitis, further refinement is needed before it can be used in diagnosis. Additional research should determine why the AI model and physician results differed. This may be due to image brightness or AI training data quality/quantity. As joint shape changes greatly with growth, and the AI model uses the bone surface as a landmark, pediatric (<10 years old) images were excluded; the model has low discrimination accuracy if a bone surface cannot be detected. In future, features associated with joint growth should be incorporated into the algorithm to allow more accurate pediatric diagnosis. The future ability of an AI model to diagnose hemarthrosis and synovitis in clinical practice would support appropriate therapeutic intervention. This would be expected to help with the achievement of a healthy and active life for people with hemophilia.

FACILE: A capsule network with fewer capsules and richer hierarchical information for malware image classification

The struggle between security researchers and malware perpetuates an endless arms race. Recent studies indicate that converting malware into grayscale images and using convolutional neural network or capsule network to classify and identify them is a promising approach. Nonetheless, training convolutional neural networks demands a significant amount of data and parameters, posing a challenge to obtaining enough training samples for malware detection tasks, particularly for new malware families. Additionally, a high number of parameters translates to low classification efficiency. Capsule networks offer a promising approach to achieve higher-level feature representations through dynamic routing between capsules, but research on capsule networks is still nascent, and there are two major challenges for malware classification tasks: the huge number of parameters, which makes model training difficult, and capsules shedding, which results in information loss during routing. To address these issues, we propose a capsule network called FACILE that uses fewer capsules and richer hierarchical information. In the initial feature extraction stage, we perform multi-level feature extraction and fusion using dynamic convolution. In the routing phase, we introduce balance coefficients to enhance the model's representational power and stabilize the training process. We conducted experiments on the VIRUS-MNIST, MalImg, and BIG2015 datasets and found that FACILE requires only 8.1% of the capsules and 1.8%-3.3% of the parameters compared to the original CapsNet, while reducing the error rate to 8.087%, 1.149%, and 2.797%, respectively. Furthermore, FACILE demonstrates competitive performance with a small number of parameters as the training samples decrease compared to convolutional neural network-based models such as ResNet, EfficientNet, and VGG.

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks.

This article presents an integrated system that uses the capabilities of unmanned aerial vehicles (UAVs) to perform a comprehensive crop analysis, combining qualitative and quantitative evaluations for efficient agricultural management. A convolutional neural network-based model, Detectron2, serves as the foundation for detecting and segmenting objects of interest in acquired aerial images. This model was trained on a dataset prepared using the COCO format, which features a variety of annotated objects. The system architecture comprises a frontend and a backend component. The frontend facilitates user interaction and annotation of objects on multispectral images. The backend involves image loading, project management, polygon handling, and multispectral image processing. For qualitative analysis, users can delineate regions of interest using polygons, which are then subjected to analysis using the Normalized Difference Vegetation Index (NDVI) or Optimized Soil Adjusted Vegetation Index (OSAVI). For quantitative analysis, the system deploys a pre-trained model capable of object detection, allowing for the counting and localization of specific objects, with a focus on young lettuce crops. The prediction quality of the model has been calculated using the AP (Average Precision) metric. The trained neural network exhibited robust performance in detecting objects, even within small images.

WT-CNN: A Hybrid Machine Learning Model for Heart Disease Prediction

Heart disease remains a predominant health challenge, being the leading cause of death worldwide. According to the World Health Organization (WHO), cardiovascular diseases (CVDs) take an estimated 17.9 million lives each year, accounting for 32% of all global deaths. Thus, there is a global health concern necessitating accurate prediction models for timely intervention. Several data mining techniques are used by researchers to help healthcare professionals to predict heart disease. However, the traditional machine learning models for predicting heart disease often struggle with handling imbalanced datasets. Moreover, when prediction is on the bases of complex data like ECG, feature extraction and selecting the most pertinent features that accurately represent the underlying pathophysiological conditions without succumbing to overfitting is also a challenge. In this paper, a continuous wavelet transformation and convolutional neural network-based hybrid model abbreviated as WT-CNN is proposed. The key phases of WT-CNN are ECG data collection, preprocessing, RUSBoost-based data balancing, CWT-based feature extraction, and CNN-based final prediction. Through extensive experimentation and evaluation, the proposed model achieves an exceptional accuracy of 97.2% in predicting heart disease. The experimental results show that the approach improves classification accuracy compared to other classification approaches and that the presented model can be successfully used by healthcare professionals for predicting heart disease. Furthermore, this work can have a potential impact on improving heart disease prediction and ultimately enhancing patient lifestyle.

A computer vision approach to continuously monitor fatigue during resistance training

Monitoring fatigue during resistance training is essential to avoid injuries caused by overtraining. Fatigue can be comprehensively quantified by the external and internal load, where the external load is the work done by the athlete, and the internal load is the psychological and physiological response to the external load. This paper proposes a computer vision method to continuously monitor fatigue during resistance training by predicting external and internal parameters, namely the generated power and the rating of perceived exertion. We utilize the human pose estimation from two Microsoft Azure Kinect cameras to capture the movement of athletes while performing stationary exercises. Our method processes the obtained kinematic data, computes skeleton features to train traditional machine learning algorithms, and constructs feature maps to train convolutional neural network-based models to predict the load parameters. For evaluation, we recorded a dataset of 16 subjects who performed squat exercises on a Flywheel and rated their perceived exertion after each set. A measuring unit integrated into the Flywheel provided power readings for each repetition. The results show that our method achieves good results in predicting both parameters. Gradient Boosting Regression Trees best predicted perceived exertion with a mean absolute percentage error of 8.08% and a Spearman’s ρ=0.74. Multi-layer Perceptron performed best in predicting power with a mean absolute error of 23.13 Watts and ρ=0.79. Our findings show that our approach delivers promising external and internal load quantifications for fatigue, with great potential to provide external feedback to coaches or athletes.

Abstract 16324: A Deep Learning Electrocardiogram Model Identifies Risk of Incident Heart Failure in Patients With Subclinical Hypothyroidism

Introduction: Subclinical hypothyroidism (SH) is associated with an increased risk of heart failure (HF). However, there are no established measures to predict HF development within the SH population. Hypothesis: Risk of incident heart failure in SH can be predicted from a 12-lead electrocardiogram using features extracted by a convolutional neural network-based model. Methods: To test the hypothesis, we developed a convolutional neural network (CNN)-based model to detect ECG features linked with low thyroid function by training to discriminate the presence of hypothyroidism from 10,745 ECGs (2,693 from patients with low free T4 (FT4) and 8,052 from age, sex-matched patients without evidence of low FT4). The model was applied to baseline ECGs from an external cohort of 2,293 patients with SH (defined TSH >4.5mIU/L and FT4 within 0.9-1.9ng/dl) to estimate the cardiac phenotype induced by hypothyroidism (ECG-hypothyroidism score). Cumulative incidences of HF admission were compared between groups stratified by the tertile of the model output (low, intermediate, and high score groups). Adjusted hazard ratios (aHR) were calculated using Cox proportional hazard model. Results: HF admission was more frequent in the high score group (9.60/100 person-year), compared with the intermediate or low score groups (5.08 and 2.29 per 100 person-year in the, respectively; Fig ). Subgroup analyses by sex and age group yielded similar results. The association between the ECG-hypothyroidism score and HF admission remained significant after adjustment for patient characteristics, conventional ECG abnormalities, and baseline thyroid function (high vs low score group: aHR 3.56, [95%CI, 2.26-5.61]; high vs intermediate score group: aHR 1.78, [95%CI, 1.25-2.54]). Conclusions: The CNN-based model, trained to detect prevalent hypothyroidism, accurately identified patients at high risk for HF admission within the SH population using baseline ECG.

Identification of glaucoma in fundus images utilizing gray wolf optimization with deep convolutional neural network-based resnet50 model

Identification of glaucoma in fundus images utilizing gray wolf optimization with deep convolutional neural network-based resnet50 model

Development of a lightweight convolutional neural network-based visual model for sediment concentration prediction by incorporating the IoT concept

Abstract Sediment concentration (SC) monitoring has always been a pressing issue in water resource management, as many existing instruments still face challenges in accurately measuring due to environmental factors and instrument limitations. A robust technology is worth presenting to apply in the field site. This study firstly uses mean-absolute-error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and Nash–Sutcliffe efficiency coefficient (NSE) to describe the performance of the proposed convolutional neural network (CNN). Moreover, adapting the ensemble learning concept to compare the multiple machine learning (ML) approaches, the CNN presents the highest predicted accuracy, 91%, better than SVM (79%), VGG19 (63%) and ResNet50 (35%). As a result, the proposed CNN framework can appropriately apply the monitoring needs. The primary purpose is to develop a simple, accurate, and stable SC monitoring technology. Instead of some complex architectures, a simple and small neural network is adopted to implement real-time application (RTA). Via our design, such a traditional but critical issue can be improved to a new state. For example, by incorporating the concept of the Internet of Things (IoT) with our design, the distributed computing system for large-scale environmental monitoring can be realized quickly and easily.

Structural and functional connectome relationships in early childhood

There is strong evidence that the functional connectome is highly related to the white matter connectome in older children and adults, though little is known about structure-function relationships in early childhood. We investigated the development of cortical structure-function coupling in children longitudinally scanned at 1, 2, 4, and 6 years of age (N = 360) and in a comparison sample of adults (N = 89). We also applied a novel graph convolutional neural network-based deep learning model with a new loss function to better capture inter-subject heterogeneity and predict an individual’s functional connectivity from the corresponding structural connectivity. We found regional patterns of structure-function coupling in early childhood that were consistent with adult patterns. In addition, our deep learning model improved the prediction of individual functional connectivity from its structural counterpart compared to existing models.

Dysarthria detection using convolution neural network

Dysarthria patients have difficulty controlling their speaking muscles, resulting in incomprehensible speech. A number of studies have looked into speech impairments; however, more research is needed to consider speakers with the same impairment but different levels of impairment. The type of impairment and severity level will aid in determining the dysarthria's progression as well as treatment planning. The use of a Convolutional Neural Network-based model to detect dysarthria is proposed in this paper. Early detection is the first step toward better impairment management. For the analysis of speech signals, the proposed model uses a number of speech features such as zero crossing rates, MFCCs, spectral centroids, and spectral roll off. For training and testing, the proposed model is used the TORGO speech signal database. With an accuracy score of 93.87%, CNN shows promising results in early dysarthric speech diagnosis.

Novel framework based on ensemble classification and secure feature extraction for COVID-19 critical health prediction

The Covid outbreak necessitated the use of an automated method for treating patients with critical symptoms. Increasing use of the Internet of Things (IoT) and smart devices requires access to real-time data over robust and secure network infrastructures. During the lifetime of healthcare applications, the data must be transferred in a controlled manner. In this context, this study proposes, an IoT-based decision support system for the analysis of Covid-related data in healthcare systems. Through in-vehicle monitoring, it is intended to safely send important data and patient updates to attending physicians. The suggested approach uses feature extraction methods to identify patients' crucial states while protecting their private information. To improve feature extraction, two techniques are combined: ensemble classification and cipher substitution for secure Json Web Tokens (JWTs). By incorporating the Secure Hashing Algorithm-2 (SHA-2) methods into the Feature Extraction based Logistic Regression (FELR), security protections are substantially reinforced. The system uses the Message Queuing Telemetry Transport (MQTT) protocol to provide secure communication between devices and access to the extracted characteristics from rescue vehicles. The system analyzes passengers, traffic data, and medical issues, which are continually updated with cloud-based security, by using vehicle-based communication networks. The secure movement of patient data inside the hospital network is ensured by this cloud-based system. By using sophisticated regression analysis and ensemble learning approaches, our suggested methodology has shown significant advantages compared to traditional models such as Logistic Regression (LR) and Mixed Regression (MR). Our technique demonstrates exceptional performance with an accuracy rate of 96%, a recall rate of 91%, and an F-measure of 91%. We also precisely compared the performance of our proposed FELR algorithm with hybrid prediction model using deep learning, the extra tree convolutional neural network-based ensemble model, ensemble-based random forest, ensemble transformation learning, and machine learning with regression. Significantly, FELR algorithm has demonstrated exceptional superiority by consistently surpassing the predictive capabilities of all these algorithms. The system has exceptional performance and exhibits improved efficiency and secure transmission capabilities. The unique model that we propose offers a feasible and effective approach for detecting crucial states in patients within healthcare facilities. The success of our framework is shown via a well-planned approach that includes important processes such as data preprocessing, feature extraction, ensemble model construction, and evaluation. The proposed framework offers a promising avenue for aiding healthcare professionals in making informed decisions regarding the critical health trajectories of COVID-19 patients.

Based on the multi-scale information sharing network of fine-grained attention for agricultural pest detection.

It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural products, and have important practical significance for improving the quality of agricultural products and increasing the output of agricultural products. At the same time, it provides a kind of effective prevention and control measures for farmers, so as to ensure the safety and health of crops. Because of the slow speed and high cost of manual identification, it is necessary to establish a set of automatic pest identification system. The traditional image-based insect classifier is mainly realized by machine vision technology, but because of its high complexity, the classification efficiency is low and it is difficult to meet the needs of applications. Therefore, it is necessary to develop a new automatic insect recognition system to improve the accuracy of insect classification. There are many species and forms of insects, and the field living environment is complex. The morphological similarity between species is high, which brings difficulties to the classification of insects. In recent years, with the rapid development of deep learning technology, using artificial neural network to classify pests is an important method to establish a fast and accurate classification model. In this work, we propose a novel convolutional neural network-based model (MSSN), which includes attention mechanism, feature pyramid, and fine-grained model. The model has good scalability, can better capture the semantic information in the image, and achieve more accurate classification. We evaluated our approach on a common data set: large-scale pest data set, PlantVillage benchmark data set, and evaluated model performance using a variety of evaluation indicators, namely, macro mean accuracy (MPre), macro mean recall rate (MRec), macro mean F1-score (MF1), Accuracy (Acc) and geometric mean (GM). Experimental results show that the proposed algorithm has better performance and universality ability than the existing algorithm. For example, on the data set, the maximum accuracy we obtained was 86.35%, which exceeded the corresponding technical level. The ablation experiment was conducted on the experiment itself, and the comprehensive evaluation of the complete MSSN(scale 1+2+3) was the best in various performance indexes, demonstrating the feasibility of the innovative method in this paper.

Retracted: A Convolutional Neural Network-Based Model for Supply Chain Financial Risk Early Warning.

[This retracts the article DOI: 10.1155/2022/7825597.].

A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer.

Convolutional neural networks are a class of deep neural networks used for different clinical purposes, including improving the detection rate of colorectal lesions. This systematic review and meta-analysis aimed to assess the performance of convolutional neural network-based models in the detection or classification of colorectal polyps and colorectal cancer. A systematic search was performed in MEDLINE, SCOPUS, Web of Science, and other related databases. The performance measures of the convolutional neural network models in the detection of colorectal polyps and colorectal cancer were calculated in the 2 scenarios of the best and worst accuracy. Stata and R software were used for conducting the meta-analysis. From 3368 searched records, 24 primary studies were included. The sensitivity and specificity of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged from 84.7% to 91.6% and from 86.0% to 93.8%, respectively. These values in predicting colorectal cancer varied between 93.2% and 94.1% and between 94.6% and 97.7%. The positive and negative likelihood ratios varied between 6.2 and 14.5 and 0.09 and 0.17 in these scenarios, respectively, in predicting colorectal polyps, and 17.1-41.2 and 0.07-0.06 in predicting colorectal polyps. The diagnostic odds ratio and accuracy measures of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged between 36% and 162% and between 80.5% and 88.6%, respectively. These values in predicting colorectal cancer in the worst and the best scenarios varied between 239.63% and 677.47% and between 88.2% and 96.4%. The area under the receiver operating characteristic varied between 0.92 and 0.97 in the worst and the best scenarios in colorectal polyps, respectively, and between 0.98 and 0.99 in colorectal polyps prediction. Convolutional neural network-based models showed an acceptable accuracy in detecting colorectal polyps and colorectal cancer.