Convolutional Neural Networks For Identification Research Articles

Gaussian Aquila optimizer based dual convolutional neural networks for identification and grading of osteoarthritis using knee joint images

Degenerative musculoskeletal disease known as Osteoarthritis (OA) causes serious pain and abnormalities for humans and on detecting at an early stage, timely treatment shall be initiated to the patients at the earliest to overcome this pain. In this research study, X-ray images are captured from the humans and the proposed Gaussian Aquila Optimizer based Dual Convolutional Neural Networks is employed for detecting and classifying the osteoarthritis patients. The new Gaussian Aquila Optimizer (GAO) is devised to include Gaussian mutation at the exploitation stage of Aquila optimizer, which results in attaining the best global optimal value. Novel Dual Convolutional Neural Network (DCNN) is devised to balance the convolutional layers in each convolutional model and the weight and bias parameters of the new DCNN model are optimized using the developed GAO. The novelty of the proposed work lies in evolving a new optimizer, Gaussian Aquila Optimizer for parameter optimization of the devised DCNN model and the new DCNN model is structured to minimize the computational burden incurred in spite of it possessing dual layers but with minimal number of layers. The knee dataset comprises of total 2283 knee images, out of which 1267 are normal knee images and 1016 are the osteoarthritis images with an image of 512 × 512-pixel width and height respectively. The proposed novel GAO-DCNN system attains the classification results of 98.25% of sensitivity, 98.93% of specificity and 98.77% of classification accuracy for abnormal knee case–knee joint images. Experimental simulation results carried out confirms the superiority of the developed hybrid GAO-DCNN over the existing deep learning neural models form previous literature studies.

Quantification of cauda equina nerve root dispersion through radiomics features in weight-bearing MRI in normal subjects and spinal canal stenosis patients.

To quantify the distribution of cauda equina nerve roots in supine and upright positions using manual measurements and radiomics features both in normal subjects and in lumbar spinal canal stenosis (LSCS) patients. We retrospectively recruited patients who underwent weight-bearing MRI in supine and upright positions for back pain. 3D T2-weighted isotropic acquisition (3D-HYCE) sequences were used to develop a 3D convolutional neural network for identification and segmentation of lumbar vertebrae. Para-axial reformatted images perpendicular to the spinal canal and parallel to each vertebral endplate were automatically extracted. From each level, we computed the maximum antero-posterior (AP) and latero-lateral (LL) dispersion of nerve roots; further, radiomics features were extracted to quantify standardized metrics of nerve root distribution. We included 16 patients with LSCS and 20 normal subjects. In normal subjects, nerve root AP dispersion significantly increased from supine to upright position (p < 0.001, L2-L5 levels), and radiomics features showed an increase in non-uniformity. In LSCS subjects, in the upright position AP dispersion of nerve roots and entropy-related features increased caudally to the stenosis level (p < 0.001) and decreased cranially (p < 0.001). Moreover, entropy-related radiomics features negatively correlated with pre-operative Pain Numerical Rating Scale. Comparison between normal subjects and LSCS patients showed a difference in AP dispersion and increase of variance cranially to the stenosis level (p < 0.001) in the upright position. Nerve root distribution inside the dural sac changed between supine and upright positions, and radiomics features were able to quantify the differences between normal and LSCS subjects. The distribution of cauda equina nerve roots and the redundant nerve root sign significantly varies between supine and upright positions in normal subjects and spinal canal stenosis patients, respectively. Radiomics features quantify nerve root dispersion and correlates with pain severity. • Weight-bearing MRI depicts spatial distribution of the cauda equina in both supine and upright positions in normal subjects and spinal stenosis patients. • Radiomics features can quantify the effects of spinal stenosis on the dispersion of the cauda equina in the dural sac. • In the orthostatic position, dispersion of nerve roots is different in lumbar spinal stenosis patients compared to that in normal subjects; entropy-related features negatively correlated with pre-operative Pain Numerical Rating Scale.

A novel convolutional neural network for identification of retinal layers using sliced optical coherence tomography images

Retinal imaging is crucial for observing the retina and accurately diagnosing pathological problems. Optical Coherence Tomography (OCT) has been a transformative breakthrough for developing high-resolution cross-sectional images. It is imperative to delineate the multiple layers of the retina for a proper diagnosis. A novel segmentation-based approach is introduced in this study to identify seven distinct layers of the retina using OCT images. The developed approach presents SliceOCTNet, a customized U-shaped Convolutional Neural Network (CNN) that introduces group normalization and intricate skip connections. Paired alongside a hybrid loss function, the SliceOCTNet outperformed most state-of-the-art approaches. The introduction of Group Normalization in SliceOCTNet stabilized the model and improved layer identification even when working with small datasets. The use of skip connections also contributed to an improvement in the spatial outlook of the model. Implementing a hybrid loss function addresses the class imbalance problem in the dataset. Duke University’s spectral-domain optical coherence tomography (SD-OCT) B-scan dataset of healthy and Diabetic Macular Edema (DME) afflicted patients was utilized to train and evaluate the SliceOCTNet. The model accurately recognizes the seven layers of the retina. It can achieve a high dice coefficient value of 0.941 and refine the segmentation process to a higher level of precision.

Dense Convolutional Neural Network for Identification of Raman Spectra.

The rapid development of cloud computing and deep learning makes the intelligent modes of applications widespread in various fields. The identification of Raman spectra can be realized in the cloud, due to its powerful computing, abundant spectral databases and advanced algorithms. Thus, it can reduce the dependence on the performance of the terminal instruments. However, the complexity of the detection environment can cause great interferences, which might significantly decrease the identification accuracies of algorithms. In this paper, a deep learning algorithm based on the Dense network has been proposed to satisfy the realization of this vision. The proposed Dense convolutional neural network has a very deep structure of over 40 layers and plenty of parameters to adjust the weight of different wavebands. In the kernel Dense blocks part of the network, it has a feed-forward fashion of connection for each layer to every other layer. It can alleviate the gradient vanishing or explosion problems, strengthen feature propagations, encourage feature reuses and enhance training efficiency. The network's special architecture mitigates noise interferences and ensures precise identification. The Dense network shows more accuracy and robustness compared to other CNN-based algorithms. We set up a database of 1600 Raman spectra consisting of 32 different types of liquid chemicals. They are detected using different postures as examples of interfered Raman spectra. In the 50 repeated training and testing sets, the Dense network can achieve a weighted accuracy of 99.99%. We have also tested the RRUFF database and the Dense network has a good performance. The proposed approach advances cloud-enabled Raman spectra identification, offering improved accuracy and adaptability for diverse identification tasks.

Convolutional Neural Network for Identification of Personal Protective Equipment Usage Compliance in Manufacturing Laboratory

Data from the Badan Penyelenggara Jaminan Sosial (BPJS) Ketenagakerjaan Indonesia from 2019 to 2021 shows that the number of work accident victims who claimed Work Accident Insurance (Jaminan Kecelakaan Kerja / JKK) continues to increase. The high number of work accidents is mostly caused by unsafe behavior at work sites, one of which is in terms of compliance with the use of Personal Protective Equipment (PPE). One tool that is considered important as a step in reducing work accidents is an identification system for compliance of personal protective safety equipment use that can detect PPE used by visitors or workers. This study develops an automatic identification system that is built using the Convolutional Neural Network (CNN) to identify the use of PPE in the manufacturing technology laboratory. The CNN models used are the 4th and 5th versions of You Only Look Once (YOLO) which are then compared based on two methods: train from scratch and transfer learning. The dataset used for building the detection system has 11,579 images consisting of six classes of PPE objects. Overall performance of the proposed models shows very good results. Moreover, the comparison result among the three models shows that YOLOv5 transfer learning has the best performance with the best precision (94.2 %), recall (91.8 %), and mAP (88.6%).

EyeCNN: exploring the potential of convolutional neural networks for identification of multiple eye diseases through retinal imagery.

The eyes are the most important part of the human body as these are directly connected to the brain and help us perceive the imagery in daily life whereas, eye diseases are mostly ignored and underestimated until it is too late. Diagnosing eye disorders through manual diagnosis by the physician can be very costly and time taking. Thus, to tackle this, a novel method namely EyeCNN is proposed for identifying eye diseases through retinal images using EfficientNet B3. A dataset of retinal imagery of three diseases, i.e. Diabetic Retinopathy, Glaucoma, and Cataract is used to train 12 convolutional networks while EfficientNet B3 was the topperforming model out of all 12 models with a testing accuracy of 94.30%. After preprocessing of the dataset and training of models, various experimentations were performed to see where our model stands. The evaluation was performed using some well-defined measures and the final model was deployed on the Streamlit server as a prototype for public usage. The proposed model has the potential to help diagnose eye diseases early, which can facilitate timely treatment. The use of EyeCNN for classifying eye diseases has the potential to aid ophthalmologists in diagnosing conditions accurately and efficiently. This research may also lead to a deeper understanding of these diseases and it may lead to new treatments. The webserver of EyeCNN can be accessed at ( https://abdulrafay97-eyecnn-app-rd9wgz.streamlit.app/ ).

Electromechanical Impedance Temperature Compensation and Bolt Loosening Monitoring Based on Modified Unet and Multitask Learning

Bolts are frequently subjected to loosening due to time varying external loads during service. The electromechanical impedance (EMI) technique based on piezoelectric ceramic wafers (PZT) is sensitive to the initial bolt preload looseness. However, the change in environmental temperature has a great effect on EMI monitoring. Deep convolutional neural network (CNN) is a promising technique for EMI monitoring. Nevertheless, it is difficult to train a deep CNN with limited training data to accurately identify damages under a wide range of temperature variations. To this end, this study proposes a multitask CNN for identifying bolts loosening. The network consists of a temperature compensation subnetwork to compensate for the temperature effect, and a lightweight damage identification subnetwork to identify bolt loosening states. The temperature compensation subnetwork is a modified Unet, and both the impedance and temperature are used as its input. The damage identification subnetwork is connected in series behind the temperature compensation subnetwork. A multiloss function is proposed in which a TV regularizer is used. Experimental results show that the validation accuracy of the multitask network is 97.71% when the network is trained by only about 30 samples from each loosening state. Moreover, the generalization abilities of the proposed multitask model to unexpected temperatures and bolt torques are investigated. The model is interpreted by the integrated gradients method, and is also compared with single-task damage identification CNNs. It is proved that the multitask network trained by limited samples can achieve accurate damage identification in temperature varying environments.

Convolutional neural networks for identification of moving combustion chambers entering a brazing process

Despite the effective application of deep learning to image classification tasks, these techniques are yet to be fully implemented in the industrial environment. In particular in brazing processes, where the high temperatures destroy the majority of the identification systems, product identification is required to allow automatic product traceability. This work addresses this issue in an international company facility producing water heaters, overcoming the lack of information regarding which model of the combustion chamber is starting the brazing process. Relying on deep learning algorithms, a solution is presented to develop and implement a vision system capable of classifying, through convolutional neural networks (CNN), the 16 models of combustion chambers. The system deployed at the company's production line, although trained with a relatively small collection of images, achieved an F1-score weighted average of 96% and 100% accuracy in classifying some of the models of combustion chambers, already implemented on the shop floor. From the building of the vision system for image acquisition until the training and deployment of the algorithms to classifying the combustion chambers are discussed.

An efficient densely connected convolutional neural network for identification of plant diseases

An efficient densely connected convolutional neural network for identification of plant diseases

50 Rise of the (Learning) Machines: Artificial Intelligence for the Assessment of Adult Thermal Burns

IntroductionBurn depth assessment (BDA) is an essential component of the physical exam used in the treatment and triage of burn injured patients. And while many specialties incorporate labs and imaging to determine diagnoses, burn professionals must rely on a physical exam that is accurate in only 70-80% of cases. Our goal was to assess the accuracy of a new imaging technology called Multispectral imaging (MSI) combined with a machine learning algorithm to aid in rapid BDA. We present the results of the first multi-center study using this technology in adult burn injuries.MethodsIn a multi-center IRB-approved study, an MSI device was used to image subjects >18 years of age with thermal burn injuries. The imaging device captured a set of images measuring the reflectance of visible and near-IR light. Subjects were enrolled and imaged within 72 hours of injury with serial imaging as permitted. The images were used to develop a type of machine learning algorithm called a convolutional neural network (CNN) that could identify the regions of non-healing burn within an image. Non-healing burn areas were determined by a panel of three burn surgeons using two standards: a) images confirming 21-day spontaneous healing; or b) pathology reports detailing histologic changes from multiple punch biopsies taken prior to burn excision. From this data, an ensemble of eight separate CNN algorithms was used to automatically identify non-healing burn tissue. Training and test accuracies of the ensemble CNN were calculated using cross-validation at the level of the subject.ResultsOne hundred (100) adults were enrolled and imaged. The population had a mean age 45.6 ± 16.7; mean TBSA 13.0 ± 9.3; and was 31% female. From these adults, 210 burn regions were serially imaged. The estimated performance result from the ensemble CNN for identification of non-healing burn regions was AUC of 0.96. Based on the ROC curve, an ideal threshold showed an accuracy of 92.0%, sensitivity 91.9%, and specificity 92.0%.ConclusionsOur study demonstrates a non-invasive technology that rapidly determines an accurate DBA relative to traditional bedside exam. More accurate burn wound assessment could lead to avoiding unnecessary surgeries or delays in treatment and dramatic cost savings. Use of such a device in a disaster has additional value to better align a patient’s burn care needs and available resources.

Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images

In this paper, automated detection of interstitial lung disease patterns in high resolution computed tomography images is achieved by developing a faster region-based convolutional network based detector with GoogLeNet as a backbone. GoogLeNet is simplified by removing few inception models and used as the backbone of the detector network. The proposed framework is developed to detect several interstitial lung disease patterns without doing lung field segmentation. The proposed method is able to detect the five most prevalent interstitial lung disease patterns: fibrosis, emphysema, consolidation, micronodules and ground-glass opacity, as well as normal. Five-fold cross-validation has been used to avoid bias and reduce over-fitting. The proposed framework performance is measured in terms of F-score on the publicly available MedGIFT database. It outperforms state-of-the-art techniques. The detection is performed at slice level and could be used for screening and differential diagnosis of interstitial lung disease patterns using high resolution computed tomography images.

Optimized convolutional neural network for identification of maize leaf diseases with adaptive ageist spider monkey optimization model

Optimized convolutional neural network for identification of maize leaf diseases with adaptive ageist spider monkey optimization model

Application of artificial neural networks for the analysis of multispectral images

A model of a multispectral camera that can capture images at five wavelengths—532, 612, 780, 850, and 940 nm—is designed and fabricated. The obtained multispectral images are analyzed. Algorithms of artificial neural network operation in the processing of multispectral images are discussed. A convolutional neural network for identification and classification of multispectral images in real-time is developed.

A Building Roof Identification CNN Based on Interior-Edge-Adjacency Features Using Hyperspectral Imagery

Hyperspectral remote sensing can obtain both spatial and spectral information of ground objects. It is an important prerequisite for a hyperspectral remote sensing application to make good use of spectral and image features. Therefore, we improved the Convolutional Neural Network (CNN) model by extracting interior-edge-adjacency features of building roof and proposed a new CNN model with a flexible structure: Building Roof Identification CNN (BRI-CNN). Our experimental results demonstrated that the BRI-CNN can not only extract interior-edge-adjacency features of building roof, but also change the weight of these different features during the training process, according to selected samples. Our approach was tested using the Indian Pines (IP) data set and our comparative study indicates that the BRI-CNN model achieves at least 0.2% higher overall accuracy than that of the capsule network model, and more than 2% than that of CNN models.

Research on Deep Convolutional Neural Network in Damage Identification of Truss Structure

Aiming at the problem of damage identification inside the truss, through the convolutional neural network, after a large amount of data simulation, the deep learning convolutional neural network can effectively identify the internal damage of the truss and accurately determine the damage location as well as the damage recognition and effective prediction of other large roof trusses. First, we import the existing data into the convolutional neural network for identification, and calculate the damage identification process belonging to the truss. Then, we import another part of the data to verify the practicability and accuracy of the convolutional neural network, and obtain a series of data streams.

Ensemble Deep Learning for Brazil Currency Coin Prediction

In this present fast growing environment the automatic coin reorganization and identification machines has a vital role in all financial allied fields. At present most of coin recognition techniques are depends on physical properties of the coin like length, width, weight etc. Whereas image processing techniques are based on extraction of colour of the coin, edge features of the coin and shape of the coin. For recognition and detection of Brazil currency we have designed Machine Learning, Deep Learning (DL) models in this paper. We have designed various a Machine Learning (ML) models with Convolutional Neural Network for identification and reorganization of Brazil currency. Brazil currency consisting of 5 Centavos, 10 Centavos, 25 Centavos etc. Each of which has different shapes and designs. Different deep learning models are designed and applied ensemble to find the better accuracy. The trained ensemble model is tested on various the datasets which consists of shifting of images, rotation and translated images.

Hierarchical pyramidal subsampling in deep convolutional networks for visual pattern recognition

The main problem of known deep convolutional neural networks (CNN) is that they require a fixed-size input image. This requirement is “artificial” and can reduce recognition accuracy for images or its parts of arbitrary size/scale. The paper proposes a strategy of combining “hierarchical pyramidal subselection” to eliminate the above restriction. The structure of the neural network using the proposed combining strategy allows the generation of prediction regardless of the size/scale of the original image, and also improves the accuracy of recognition. Features of application of CNN for identification and recognition of defects of conducting pattern of printed circuit board blanks have been considered. Features of defects of conductive pattern of printed circuit board blanks have been briefly discussed. The invention proposes the use of artificial CNN, which have advantages in speed and accuracy in solving problems of object recognition on images relative to existing methods. The focus is on the architecture of CNN using hierarchical pyramidal subselection. Capabilities of application of CNN for recognition of defects of conducting pattern of printed circuit board blanks have been shown. Proposed method of hierarchical pyramidal subselection in deep convolutional networks has been implemented in software complex, which allows processing digital data of photographs of conducting pattern of printed circuit boards, in particular during their flaw detection, and can be used for localization of existing defects of conducting pattern. The conclusion draws the possibilities of using methods and means of image processing in flaw detection of radio-electronic equipment and instruments

MobileNets: Efficient Convolutional Neural Network for Identification of Protected Birds

Wildlife trade is one of the main factors causing endangered bird species. In Indonesia, trade has caused 28 bird species to be classified in the endangered bird category. Protection efforts have been made with the establishment of 564 species of Indonesian birds as protected birds. For law enforcement, certainty is needed in the identification of these bird species. This study begins with a Forum of Discussion Groups from relevant institutions in Java and Bali to determine the types of protected birds that are prioritized to developed in this application. Based on the results of the Forum of Discussion Group, a bird photo dataset compiled using 17 categories or types of bird photos as prioritized in this study. The method used in this study is the Convolutional Neural Network (CNN) method, which combined the structure of MobileNet and the weight of the network that has previously trained using ImageNet. The results of this study are the differences of results between CNN standards and those combined with the structure of MobileNet. For better accuracy, using the CNN standard, which is around 98.38% for the accuracy of the training, while in terms of size, combined with MobileNet has a relatively smaller model size, which is 68 megabytes.

Multi-branch Convolutional Neural Network for Identification of Small Non-coding RNA genomic loci

Genomic regions that encode small RNA genes exhibit characteristic patterns in their sequence, secondary structure, and evolutionary conservation. Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs (pre-miRNAs and snoRNAs) using models trained on the human genome. MuStARD can be used to filter small RNA-Seq datasets for identification of novel small RNA loci, intra- and inter- species, as demonstrated in three use cases of human, mouse, and fly pre-miRNA prediction. MuStARD is easy to deploy and extend to a variety of genomic classification questions. Code and trained models are freely available at gitlab.com/RBP_Bioinformatics/mustard.

Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review.

BACKGROUNDArtificial intelligence, such as convolutional neural networks (CNNs), has been used in the interpretation of images and the diagnosis of hepatocellular cancer (HCC) and liver masses. CNN, a machine-learning algorithm similar to deep learning, has demonstrated its capability to recognise specific features that can detect pathological lesions.AIMTo assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODSThe databases PubMed, EMBASE, and the Web of Science and research books were systematically searched using related keywords. Studies analysing pathological anatomy, cellular, and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer, differentiating cancer from other lesions, or staging the lesion. The data were extracted as per a predefined extraction. The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed. The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTSA total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified. The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions (n = 6), HCC from cirrhosis or development of new tumours (n = 3), and HCC nuclei grading or segmentation (n = 2). The CNNs showed satisfactory levels of accuracy. The studies aimed at detecting lesions (n = 4), classification (n = 5), and segmentation (n = 2). Several methods were used to assess the accuracy of CNN models used.CONCLUSIONThe role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies. While a few limitations have been identified in these studies, overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images.