Ossification area localization in pediatric hand radiographs using deep neural networks for object detection

Bone age assessment is of great significance to genetic diagnosis and endocrine diseases. Traditional bone age diagnosis mainly relies on experienced radiologists to examine the regions of interest in hand radiography, but it is time-consuming and may even lead to a vast error between the diagnosis result and the reference. The existing computer-aided methods predict bone age based on general regions of interest but do not explore specific regions of interest in hand radiography. This paper aims to solve such problems by performing bone age prediction on the articular surface and epiphysis from hand radiography using deep convolutional neural networks. The articular surface and epiphysis datasets are established from the Radiological Society of North America (RSNA) pediatric bone age challenge, where the specific feature regions of the articular surface and epiphysis are manually segmented from hand radiography. Five convolutional neural networks, i.e., ResNet50, SENet, DenseNet-121, EfficientNet-b4, and CSPNet, are employed to improve the accuracy and efficiency of bone age diagnosis in clinical applications. Experiments show that the best-performing model can yield a mean absolute error (MAE) of 7.34 months on the proposed articular surface and epiphysis datasets, which is more accurate and fast than the radiologists. The project is available at https://github.com/YameiDeng/BAANet/, and the annotated dataset is also published at https://doi.org/10.5281/zenodo.7947923.

Advances in satellite imagery presents unprecedented opportunities for understanding natural and social phenomena at global and regional scales. Although the field of satellite remote sensing has evaluated imperative questions to human and environmental sustainability, scaling those techniques to very high spatial resolutions at regional scales remains a challenge. Satellite imagery is now more accessible with greater spatial, spectral and temporal resolution creating a data bottleneck in identifying the content of images. Because satellite images are unlabeled, unsupervised methods allow us to organize images into coherent groups or clusters. However, the performance of unsupervised methods, like all other machine learning methods, depends on features. Recent studies using features from pre-trained networks have shown promise for learning in new datasets. This suggests that features from pre-trained networks can be used for learning in temporally and spatially dynamic data sources such as satellite imagery. It is not clear, however, which features from which layer and network architecture should be used for learning new tasks. In this paper, we present an approach to evaluate the transferability of features from pre-trained Deep Convolutional Neural Networks for satellite imagery. We explore and evaluate different features and feature combinations extracted from various deep network architectures, and systematically evaluate over 2,000 network-layer combinations. In addition, we test the transferability of our engineered features and learned features from an unlabeled dataset to a different labeled dataset. Our feature engineering and learning are done on the unlabeled Draper Satellite Chronology dataset, and we test on the labeled UC Merced Land dataset to achieve near state-of-the-art classification results. These results suggest that even without any or minimal training, these networks can generalize well to other datasets. This method could be useful in the task of clustering unlabeled images and other unsupervised machine learning tasks.

A computer-based intelligent visual inspection system plays a major role in evaluating the quality of textile fabrics and its demand is continuously increasing in the textile industry, especially when the quality of textile is to be considered. In this paper, we propose an AI-based automated fabric defect detection algorithm which utilizes pre-trained deep neural network models for classifying possible fabric defects. The fabric images are enhanced by pre-processing at various levels using conventional image processing techniques and they are used to train the networks. The Deep Convolutional Neural Network (DCNN) and a pre-trained network, AlexNet, are used to train and classify various fabric defects. With the exiting textile dataset, a maximum classification accuracy of 92.60% is achieved in the conducted simulations. With this accuracy, the detection and classification system based on this classifier model can aid the human to find faults in the fabric manufacturing unit.

Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison

Deep Neural Networks for object detection have become of significant interest with the substantial improvement in their efficiency and increase in their applications. However, training such a network requires a large annotated dataset, which is very expensive in term of time and human effort. In this context, several automatic image annotation solutions have been proposed which are dependent on rich visual features and thus suitable for color images only. On the other hand, recent experiments have proven that color cameras alone are not enough for pedestrian related applications and there is a need to use other visual sensors as well, such as thermal cameras. In this paper, we propose an automatic image annotation technique for pedestrian detection in thermal images using an adaptive background/foreground estimation model to train Faster-RCNN. The results presented in this paper, obtained through longterm experiments demonstrate the efficacy of our technique. The results also show that our proposed technique is be very useful to generate image annotations automatically and to train a deep neural network without having a manually annotated dataset for cameras with different modalities.

Recognition of handwritten characters is one of the difficult and challenging task because of the variation of characters in size, style and pattern. The complexity increased further with dictionary (alphabets, numerals, special characters), more individuals, age groups, and also with working environment. The exploration of open-source pre-trained networks for the classification of characters was minimal. This motivated us to explore the pre-trained deep convolutional networks (Alexnet, VGG-16, Resnet-50), and fine-tune them to recognize the handwritten characters using transfer learning. The experimentation results of widely used database EMNIST using pre-trained networks are in-par with the results of the state-of-art customized networks,which is specific to database and language. The classification accuracy of Resnet-50 for EMNIST (By-class: 87.24%, By-merge: 90.64%, Balanced: 89.18%, Letters: 94.90%, Digits: 99.57%).

Traditional Image Processing Techniques (IPT), used for automating the detection and classification of weld defects from radiography images, have their own limitations, which can be overcome by Deep Neural Networks (DNN). DNN produces considerably good results in fields which offer big dataset for it to train. DNN trained with small datasets by conventional methods produces less accurate results. This limits the use of DNN in many fields. This study focuses to overcome this limitation, by adopting transfer learning using Pre-trained deep convolutional neural networks. By this method, a weld defect radiographic image classifier, which can classify 14 types of weld defects, was constructed. 940 Image patches of weld defects were manually collected and labelled from GDXray database. Subsequently the features of this weld defect dataset were extracted using VGG16 and ResNet50 CNNs, both pre-trained on the ImageNet database. Then machine learning models such as Logistic Regression, Support Vector Machine (SVM) and Random Forest were trained on these extracted features. The Classifier based on SVM trained on features extracted by ResNet50 outperforms the other counter parts with an accuracy of 98%. In all these cases, transfer learning improves performance and reduces the training time and computational system requirements.

IntroductionA deep learning-based automatic bone age identification system (ABAIs) was introduced in medical imaging. This ABAIs enhanced accurate, consistent, and timely clinical diagnostics and enlightened research fields of deep learning and artificial intelligence (AI) in medical imaging.AimThe goal of this study was to use the Deep Neural Network (DNN) model to assess bone age in months based on a database of pediatric left-hand radiographs.MethodsThe Inception Resnet V2 model with a Global Average Pooling layer to connect to a single fully connected layer with one neuron using the Rectified Linear Unit (ReLU) activation function consisted of the DNN model for bone age assessment (BAA) in this study. The medical data in each case contained posterior view of X-ray image of left hand, information of age, gender and weight, and clinical skeletal bone assessment.ResultsA database consisting of 8,061 hand radiographs with their gender and age (0–18 years) as the reference standard was used. The DNN model’s accuracies on the testing set were 77.4%, 95.3%, 99.1% and 99.7% within 0.5, 1, 1.5 and 2 years of the ground truth respectively. The MAE for the study subjects was 0.33 and 0.25 year for male and female models, respectively.ConclusionIn this study, Inception Resnet V2 model was used for automatic interpretation of bone age. The convolutional neural network based on feature extraction has good performance in the bone age regression model, and further improves the accuracy and efficiency of image-based bone age evaluation. This system helps to greatly reduce the burden on clinical personnel.

Novel vision sensors such as thermal, hyperspectral, polarization, and event cameras provide information that is not available from conventional intensity cameras. An obstacle to using these sensors with current powerful deep neural networks is the lack of large labeled training datasets. This paper proposes a Network Grafting Algorithm (NGA), where a new front end network driven by unconventional visual inputs replaces the front end network of a pretrained deep network that processes intensity frames. The self-supervised training uses only synchronously-recorded intensity frames and novel sensor data to maximize feature similarity between the pretrained network and the grafted network. We show that the enhanced grafted network reaches competitive average precision (AP\(_{50}\)) scores to the pretrained network on an object detection task using thermal and event camera datasets, with no increase in inference costs. Particularly, the grafted network driven by thermal frames showed a relative improvement of 49.11% over the use of intensity frames. The grafted front end has only 5–8% of the total parameters and can be trained in a few hours on a single GPU equivalent to 5% of the time that would be needed to train the entire object detector from labeled data. NGA allows new vision sensors to capitalize on previously pretrained powerful deep models, saving on training cost and widening a range of applications for novel sensors.

Inversion of magnetic data using deep neural networks

This paper proposes an ensemble transfer learning guidance algorithm for angular-constrained midcourse guidance to maximize the terminal velocity. The algorithm developed improves the generalization capability of the trained deep neural network to adapt to a new environment. First several deep neural guidance networks are trained for some specific working environments via supervised learning. A small-scale ensemble transfer learning network is then leveraged to fuse the knowledge of different pretrained deep neural network. This requires much less labeled data to transfer existing knowledge to a new working environment and hence greatly improves the learning efficiency, compared to the supervised learning philosophy. Extensive numerical simulations are performed to demonstrate the effectiveness of the proposed algorithm.

Digital forgery is increasing day by day due to the increase in the usage of digital media files and the ease of editing media files. But digital media editing is a serious issue, as digital media are the primary source of evidence for criminal cases in the courtrooms. Consequently, there is an urge to detect and localize forgeries in digital media to aid digital forensics. The main aim of the proposed work is to detect forgeries in digital images. Image splicing forgery involves copying an image region from one image and pasting it into another image. A deep learning-based technique is proposed to detect image splicing forgery. A pre-trained deep Convolution Neural Network is transferred for the proposed application. The network is trained using spliced and original images to adapt it to the image splicing detection problem. The layers in the network are modified and fine-tuned to make it perform well for the new unseen dataset. This re-designed convolution network discriminates spliced and original images accurately. Also, the proposed work locates the spliced image regions if both the source and spliced images are provided as input image pairs. The proposed work is tested on CASIA and Columbia splicing image datasets and achieved good results.

Abstract. Beach litter destroys marine ecosystems and creates aesthetic discomfort that lowers the value of the beach. In order to solve this beach litter problem, it is necessary to study the generation and distribution pattern of waste and the cause of the inflow. However, the data for the study are only sample data collected in some areas of the beach. Also, most of the data covers only the total amount of beach litter. UAV(Unmanned Aerial Vehicle) and Deep Neural Network can be effectively used to detect and monitor beach litter. Using UAV, it is possible to easily survey the entire beach. The Deep Neural Network can also identify the type of coastal litter. Therefore, using UAV and Deep Neural Network, it is possible to acquire spatial information by type of beach litter. This paper proposes a Beach litter detection algorithm based on UAV and Deep Neural Network and a Beach litter monitoring process using it. It also offers optimal shooting altitude and film duplication to detect small beach litter such as plastic bottles and styrofoam pieces found on the beach. In this study, DJI Mavic 2 Pro was used. The camera on the UAV is a 1-inch CMOS with a resolution of 20MP. The images obtained through UAV are produced as orthoimages and input into a pre-trained neural network algorithm. The Deep Neural Network used for Beach litter detection removed the Fully Connected Layer from the Convolutional Neural Network for semantic segmentation.

Deep learning-based land cover and land use (LCLU) classification systems are a significant aspiration for remote sensing communities. In nature, remote sensing images have various properties that need to be analyzed. Analyzing and interpreting image properties is difficult due to the nature of the image, the sensor technology’s capability, and other determinant variables such as seasons and weather conditions. The problem is essential for environmental monitoring, agricultural decision-making, and urban planning if it can be supported by deep learning systems. Therefore, deep learning approaches are proposed to quickly analyze and interpret the remote sensing image to classify the LCLU. The deep learning methods could be designed starting from scratch or using pre-trained networks. However, there are few comparisons of deep learning methods developed from scratch and trained on pre-trained networks. Thus, we proposed evaluating and comparing the deep learning models convolutional neural network feature extractor (CNN-FE) by developing it from scratch, transfer learning, and fine-tuning it for the LCLU classification system using remote sensed images. Using CNN-FE, TL, and fine-tuning deep learning models as examples, this paper compares and analyzes deep learning algorithms for remote sensed image classification. After developing and training each deep learning model on the UCM dataset, we evaluated and compared their performances using the performance measurement metrics accuracy, precision, recall, f1-score, and confusion matrix. The proposed deep learning algorithms can adapt and learn the features of the remote sensing images, and the TL and fine-tuning classification performances are significantly improved. As a result of the efficient time used for training the models, this paper discovered that the fine-tuned deep learning model achieved profound accuracy performance results in the UCM dataset.

In this paper, we propose a method to perform training and inference with multiple instances of the same deep neural network architecture on images taken from cameras of different directions. Across multiple cameras, depending on each of their directional characteristics, objects viewed from the cameras can form slightly different distributions in visual features. Regarding this, we emphasize the importance of camera-wise training on multiple instances of a given deep neural network for object detection. Given the Waymo Open Perception Dataset, we used multiple instances of the YOLOv5x6 architecture and trained each of them per camera. Such a training scheme on the Training Set achieves better training progression, and the inference results are shown to have AP/L1 as high as 0.6679 on the Testing Set.