Medigreen: Ai Based Medicinal Plant Identification Using Deep Learning

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.

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

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.

Protection of biodiversity is quite essential and for this purpose we should know more about the species. Identification of plant species by using conventional hand-crafted features is complex. It is difficult for non-experts to remember the specific botanical terms. The idea of automatic identification of plant species is approaching reality. Machine learning and deep learning play an important role in this matter. The deep learning Convolutional Neural Networks (CNN) can apply to extract the features from leaf images. CNN or machine learning classifiers can be used for the classification of plant species. The deep learning methods outperform all handcrafted methods. This survey is about the identification of plant species using deep learning methods.

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.

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..

Production of food crops is hampered by the proliferation of crop diseases which cause huge harvest losses. Current crop-health monitoring programs involve the deployment of scouts and experts to detect and identify crop diseases through visual observation. These monitoring schemes are expensive and too slow to offer timely remedial recommendations for preventing the spread of these crop-damaging diseases. There is thus a need for the development of cheaper and faster methods for identifying and monitoring crop diseases. Recent advances in deep learning have enabled the development of automatic and accurate image classification systems. These advances coupled with the widespread availability of multispectral aerial imagery provide a cost-effective method for developing crop-diseases classification tools. However, large datasets are required to train deep learning models, which may be costly and difficult to obtain. Fortunately, models trained on one task can be repurposed for different tasks (with limited data) using transfer learning technique. The purpose of this research was to develop and implement an end-to-end deep learning framework for early detection and continuous monitoring of crop diseases using transfer learning and high resolution, multispectral aerial imagery. In the first study, the technique was used to compare the performance of five pre-trained deep learning convolutional neural networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) in classifying crop diseases for apples, grapes, and tomatoes. The results of the study show that the best performing crop-disease classification models were those trained on the VGG16 network, while those trained on the ResNet50 network had the worst performance. The other studies compared the performance of using transfer learning and different three-band color combinations to train single- and multiple-crop classification models. The results of these studies show that models combining red, near infrared, and blue bands performed better than models trained with the traditional visible spectral band combination of red, green, and blue. The worst performing models were those combining near infrared, green, and blue bands. This research recommends that further studies be undertaken to determine the best band combinations for training single- and multi-label classification models for both crops and plants and diseases that affect them.

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.

As one of the representative algorithms of deep learning, a convolutional neural network (CNN) with the advantage of local perception and parameter sharing has been rapidly developed. CNN-based detection technology has been widely used in computer vision, natural language processing, and other fields. Fresh fruit production is an important socioeconomic activity, where CNN-based deep learning detection technology has been successfully applied to its important links. To the best of our knowledge, this review is the first on the whole production process of fresh fruit. We first introduced the network architecture and implementation principle of CNN and described the training process of a CNN-based deep learning model in detail. A large number of articles were investigated, which have made breakthroughs in response to challenges using CNN-based deep learning detection technology in important links of fresh fruit production including fruit flower detection, fruit detection, fruit harvesting, and fruit grading. Object detection based on CNN deep learning was elaborated from data acquisition to model training, and different detection methods based on CNN deep learning were compared in each link of the fresh fruit production. The investigation results of this review show that improved CNN deep learning models can give full play to detection potential by combining with the characteristics of each link of fruit production. The investigation results also imply that CNN-based detection may penetrate the challenges created by environmental issues, new area exploration, and multiple task execution of fresh fruit production in the future.

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.

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

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.

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

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.

Quality control of fruit ripeness is important to extend shelf life and ensure optimum moment of purchase. To automate and improve accuracy of this process machine learning methods have increasingly been applied in recent years. In this paper, the Inception V3 Convolutional Neural Networks (CNN) architecture is employed to assess the quality of the integrated ripeness of the fruit and shows its efficiency. The purpose of this work is to predict fruit ripeness using image processing in inception v3 model. The paper describes a portable fruit ripeness prediction system in fruits such as Apple, Banana, Guava, Lime and Orange based on Inception V3 Module. We describe an automated ripeness phase prediction system which uses the visual appearance of the fruit images and other fruits data in an integrated manner to enhance the prediction of the ripeness phases of the fruits. The images of the fruit are analysed and their ripeness is classified using the Inception V3 model, famous for its high accuracy and good performance in image classification tasks. As a side function internal material properties (like texture and water content) are determined from the data. The results demonstrate that the proposed approach outperforms state-of-the-art methods and presents more reliable and non-culture consuming method to evaluate fruit maturity. The feasibility of the real-time agricultural practices recording and decision making is there, which make enhancement in supply chain management practice better.