Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review.

PurposeThe aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs.Materials and MethodsIn order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed.ResultsWhen dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects.ConclusionClinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

With the aim of identifying possible mechanical faults in unmanned aerial vehicle (UAV) rotors during operation, this paper proposes a method based on interval sampling reconstruction of vibration signals and one-dimensional convolutional neural network (1D-CNN) deep learning. Firstly, experiments were designed to collect the vibration acceleration signals of a UAV working at high speed under three states (normal, rotor damage by varying degrees, and rotor crack by different degrees). Then, considering the powerful feature extraction and complex data analysis abilities of 1D-CNN, an effective deep learning model for fault identification is established utilizing 1D-CNN. During analysis, it is found that the recognition rate for minor faults is not ideal, with all weak states being identified as normal, reducing the overall identification accuracy, when using conventional sequential sampling to construct learning sample sets. To this end, in order to make the sample data cover the whole process of data collection as much as possible, a learning sample processing method based on interval sampling reconstruction of the vibration signal is proposed. And it is also verified that the reconstructed sample set can easily reflect the global information of mechanical operation. Finally, according to the comparison of analysis results, the recognition rate of the deep learning model for different degrees of faults is greatly improved, and minor faults could also be accurately identified through this method. The results show that the 1D-CNN deep learning model could diagnose and identify UAV rotor damage faults accurately, by incorporating the proposed method of interval sampling reconstruction.

Accurate short-term traffic flow forecasting facilitates active traffic control and trip planning. Most existing traffic flow models fail to make full use of the temporal and spatial features of traffic data. This study proposes a short-term traffic flow prediction model based on a convolution neural network (CNN) deep learning framework. In the proposed framework, the optimal input data time lags and amounts of spatial data are determined by a spatio-temporal feature selection algorithm (STFSA), and selected spatio-temporal traffic flow features are extracted from actual data and converted into a two-dimensional matrix. The CNN then learns these features to construct a predictive model. The effectiveness of the proposed method is evaluated by comparing the forecast results with actual traffic data. Other existing models are also evaluated for comparison. The proposed method outperforms baseline models in terms of accuracy.

This study aimed to compare the axial cranial ultrasound images of normal and open neural tube defect (NTD) fetuses using a deep learning (DL) model and to assess its predictive accuracy in identifying open NTD.It was a prospective case-control study. Axial trans-thalamic fetal ultrasound images of participants with open fetal NTD and normal controls between 14 and 28 weeks of gestation were taken after consent. The images were divided into training, testing, and validation datasets randomly in the ratio of 70:15:15. The images were further processed and classified using DL convolutional neural network (CNN) transfer learning (TL) models. The TL models were trained for 50 epochs. The data was analyzed in terms of Cohen kappa score, accuracy score, area under receiver operating curve (AUROC) score, F1 score validity, sensitivity, and specificity of the test.A total of 59 cases and 116 controls were fully followed. Efficient net B0, Visual Geometry Group (VGG), and Inception V3 TL models were used. Both Efficient net B0 and VGG16 models gave similar high training and validation accuracy (100 and 95.83%, respectively). Using inception V3, the training and validation accuracy was 98.28 and 95.83%, respectively. The sensitivity and specificity of Efficient NetB0 was 100 and 89%, respectively, and was the best.The analysis of the changes in axial images of the fetal cranium using the DL model, Efficient Net B0 proved to be an effective model to be used in clinical application for the identification of open NTD. · Open spina bifida is often missed due to the nonrecognition of the lemon sign on ultrasound.. · Image classification using DL identified open spina bifida with excellent accuracy.. · The research is clinically relevant in low- and middle-income countries..

Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learning convolutional neural networks (DL-CNNs) have demonstrated dermatologist-level accuracy in identifying CM. We hypothesized that support from DL-CNN could increase dermatologists' accuracy in differentiating CM from MelSim. This cross-sectional reader study analyzed the diagnostic performance of a DL-CNN and 27 dermatologists for 200 skin lesions (100 CM, 100 MelSim). The DL-CNN assigned malignancy scores ranging from 0 to 1 (> 0.5 indicating malignancy). Dermatologists assessed lesions across three levels: (I) dermoscopy only, (II) full case information (dermoscopy, close-up images, metadata), and (III) full case information plus DL-CNN scores. Primary outcomes were sensitivity, specificity, and ROC-AUC of dermatologists with or without DL-CNN-support (level-II versus -III). The DL-CNN and dermatologists in level-II showed a comparable sensitivity (95 % CI) of 90.0 % (82.6-94.5 %) and 90.1 % (86.9-93.2 %, p = 0.153), respectively. However, the DL-CNN's specificity (67.0 % (57.3-75.4 %) versus 73.2 % (69.1-77.3 %)) and ROC-AUC (0.889 (0.845-0.932) versus 0.951 (0.920-0.982)) were significantly lower than for dermatologists (all p < 0.01). When dermatologists integrated DL-CNN predictions (level-III) their sensitivity increased to 91.4 % (88.3-94.5 %, p < 0.001) without markedly changing specificity (74.2 % (70.6-77.7 %, p = 0.435)) or ROC-AUC (0.954 (0.927-0.982, p = 0.581)). Collaboration with a DL-CNN slightly improved dermatologists' diagnostic accuracy in a mixed CM and MelSim dataset, by increasing sensitivity without a loss of specificity. The DL-CNN's level of sensitivity in this difficult-to-diagnose dataset underlines the potential as an assistant diagnostic tool.

Conservation of biodiversity requires plant species identification skills, and automatic detection is a challenging and fascinating task for both computer/data scientists and botanists alike. This chapter describes a deep learning Convolutional Neural Network (CNN), which is trained to perform mobile imagery classification on plant species found throughout Ireland. The dataset of plant-classified RGB images underwent significant pre-processing, particularly in relation to background removal and data augmentation. Several models of deep learning CNN, with varying amounts of layers and training methods, have been evaluated on this dataset. Several deep learning models were trained and evaluated to document the speed and robustness of the flora identification. The highest performing model was then embedded in a web application, creating an online system to allow for new plant images to be uploaded and classified. This chapter highlights the main research challenges associated with this work, concludes with a mobile-based application, and discusses future research.KeywordsDeep learningMachine learningImage processingPlant speciesConvolutional neural networkArtificial intelligence

The study was aimed to explore the effect of information health data based on deep learning of neural network to diagnose the infectious factors of patients with chronic glomerular disease (CGD) and evaluate its diagnostic effect. Ninety patients with CGD were selected and randomly rolled into control group A, control group B, and observation group, with 30 cases in each group. Big data scientific research analysis platform was used for data integration, convolutional neural network (CNN) was employed for feature analysis, correlation analysis, and screening of disease‐related biomarkers. The patients were diagnosed by observation of symptoms and signs, combined diagnosis of blood test and urine test, and information health data diagnosis based on deep learning CNN. As a result, the specificity, sensitivity, and accuracy of information health data diagnosis based on deep learning CNN were 78.9%, 87.6%, and 92.1%, respectively. The main sources of infections in patients were lung infections, bloodstream infections, urinary system infections, skin and soft tissue infections, and upper respiratory tract infections. Amongst them, lung infection accounted for the highest proportion, reaching 65.4%, followed by blood infection (11.2%) and skin tissue infection (9.6%). The pathogens of infection were mainly bacteria, viruses, fungi, tuberculosis, and pneumocystis pneumonia (PCP), amongst which bacterial infections accounted for the highest proportion (31.5%), followed by PCP (25.6%). In short, the information health data based on deep learning CNN had high specificity, sensitivity, and accuracy for the diagnosis of CGD. The main infectious factors of CGD were pulmonary infection and blood infection, and the pathogens were mainly bacteria and viruses.

The surgical management of patients with failed total hip or knee arthroplasty (THA and TKA) necessitates the identification of the implant manufacturer and model. Failure to accurately identify implant design leads to delays in care, increased morbidity, and healthcare costs. The automated identification of implant designs has the potential to assist in the surgical management of patients with failed arthroplasty. This study aimed to develop and validate a convolutional neural network deep learning model for the identification of primary and revision hip and knee total joint arthroplasty designs from plain radiographs. This study trained a convolutional neural network deep learning model to automatically identify 24 THA designs and 14 TKA designs from 11,204 anterior-posterior radiographs obtained from 8,763 patients. From these radiographs, 8,963 radiographs (80%) were used for model training and 2,241 radiographs (20%) were used for model validation. Model performance was assessed through receiver operating curve characteristics. After 1,000 training epochs by the convolutional neural network deep learning model, the computational model discriminated 17 primary THA designs with an area under the receiver operating curve (AUC) of 0.98, sensitivity of 95.8%, and specificity of 98.6%. The deep learning model discriminated eight primary TKA designs with an AUC of 0.97, sensitivity of 94.9%, and specificity of 97.8%. The deep learning model demonstrated an AUC of 0.98 and 0.96 for the identification of seven revision THA and six revision TKA designs, respectively. This study developed and validated a convolutional neural network deep learning model for the identification of hip and knee total joint arthroplasty designs from plain radiographs. The study findings demonstrate excellent accuracy of the deep learning model for the identification of 24 THA and 14 TKA designs, illustrating the great potential of the deep learning model to assist in preoperative surgical planning of failed arthroplasty patients.

Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is important because it could help to minimize unnecessary toxic NAC and to modify regimens mid-treatment to achieve better efficacy. Machine learning (ML) is increasingly being used in radiology and medicine because it can identify relationships amongst complex data elements to inform outcomes without the need to specify such relationships a priori. One of the most popular deep learning methods that applies to medical images is the Convolutional Neural Networks (CNN). In contrast to supervised ML, deep learning CNN can operate on the whole images without requiring radiologists to manually contour the tumor on images. Although there have been many review papers on supervised ML prediction of pCR, review papers on deep learning prediction of pCR are sparse. Deep learning CNN could also incorporate multiple image types, clinical data such as demographics and molecular subtypes, as well as data from multiple treatment time points to predict pCR. The goal of this study is to perform a systematic review of deep learning methods that use whole-breast MRI images without annotation or tumor segmentation to predict pCR in breast cancer.

Coronary artery tortuosity is usually an undetected condition in patients undergoing coronary angiography. This condition requires a longer examination by the specialist to be detected. Yet, detailed knowledge of the morphology of coronary arteries is essential for planning any interventional treatment, such as stenting. We aimed to analyze coronary artery tortuosity in coronary angiography with artificial intelligence techniques to develop an algorithm capable of automatically detecting this condition in patients. This work uses deep learning techniques, in particular, convolutional neural networks, to classify patients into tortuous or non-tortuous based on their coronary angiography. The developed model was trained both on left (Spider) and right (45°/0°) coronary angiographies following a fivefold cross-validation procedure. A total of 658 coronary angiographies were included. Experimental results demonstrated satisfactory performance of our image-based tortuosity detection system, with a test accuracy of (87 ± 6)%. The deep learning model had a mean area under the curve of 0.96 ± 0.03 over the test sets. The sensitivity, specificity, positive predictive values, and negative predictive values of the model for detecting coronary artery tortuosity were (87 ± 10)%, (88 ± 10)%, (89 ± 8)%, and (88 ± 9)%, respectively. Deep learning convolutional neural networks were found to have comparable sensitivity and specificity with independent experts’ radiological visual examination for detecting coronary artery tortuosity for a conservative threshold of 0.5. These findings have promising applications in the field of cardiology and medical imaging.

Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network

The deep learning system for e-waste management presented in this proposal is a transformative solution designed to address the escalating challenges of garbage collection and management in urban environments. Rapid urbanization has resulted in increased waste generation, necessitating a more intelligent and efficient approach to e-waste collection and disposal. This system integrates cutting-edge technologies, primarily Artificial Intelligence (AI), to improve e-waste management processes, enhance resource utilization, and contribute to the creation of cleaner and more sustainable urban spaces. Urban areas are experiencing unprecedented growth, leading to a surge in the volume of waste generated daily; as such, traditional waste management systems struggle to cope with this influx, resulting in environmental pollution, compromised public health, and inefficient resource utilization. The proposed deep learning model with accuracy of 83% seeks to revolutionize existing practices by leveraging the capabilities of AI. The aim of this research is to develop a sequential neural network using a Keras and TensorFlow image analysis: a deep learning convolutional neural network (CNN) for e-waste management. The Python programming tool will be used to develop the deep learning model as well as a GUI that will facilitate human–computer interactions. The system will be tested and the result evaluated to assess the functionality and adequacy of the system.

Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.