Search

• Presents a novel framework combining residual network (ResNet) with bidirectional long short-term memory network (BiLSTM) for enhanced terahertz signal analysis. • Introduces a sophisticated method for automatic defect detection and interface localization in non-metallic materials, surpassing traditional manual analysis in efficiency and accuracy. • Achieves interface positioning accuracy surpassing 80 µm in coating sample thickness, demonstrating superior performance to conventional approaches. • Provides robust validation against X-ray CT results, revealing an error margin of only 1.07% to 5.39% in detecting adhesive layer defects. This manuscript presents a framework that integrates a residual network (ResNet) with a bidirectional long short-term memory network (BiLSTM) for effective feature extraction and interface positioning in terahertz signal analysis. This innovative approach employs spatial and temporal feature extraction modules to process terahertz signals, enabling the automatic detection of interfaces and defects in both coating and bonded structure samples. The experimental results indicate that the interface positioning accuracy of the method aligns with the manual analysis outcomes for coating samples exceeding 80 µm in thickness. Furthermore, it exhibits a high degree of consistency with X-ray CT detection results when detecting defects in the adhesive layer, with an error margin ranging from 1.07 % to 5.39 %. Significantly, we have also conducted preliminary analyses on actual samples, further underscoring the robustness and generalizability of our model. This method offers an efficient automated tool for terahertz signal analysis and holds promise for industrial detection applications. Future research will aim to broaden the range of samples and application scenarios, thereby increasing the technology's practicality in quality control and production line detection. [ABSTRACT FROM AUTHOR]

• COVID-19 images were analyzed with a new preprocessing model. • In this study, feature extraction was performed from COVID-19 images according to RGB values. • The proposed model was tested by statistical methods. • The results of both normal data and feature-reduced data were compared. The COVID-19 pandemic spread all over the world, starting in China in late 2019, and significantly affected life in all aspects. As seen in SARS, MERS, COVID-19 outbreaks, coronaviruses pose a great threat to world health. The COVID-19 epidemic, which caused pandemics all over the world, continues to seriously threaten people's lives. Due to the rapid spread of COVID-19, many countries' healthcare sectors were caught off guard. This situation put a burden on doctors and healthcare professionals that they could not handle. All of the studies on COVID-19 in the literature have been done to help experts to recognize COVID-19 more accurately, to use more accurate diagnosis and appropriate treatment methods. The alleviation of this workload will be possible by developing computer aided early and accurate diagnosis systems with machine learning. Diagnosis and evaluation of pneumonia on computed tomography images provide significant benefits in investigating possible complications and in case follow-up. Pneumonia and lesions occurring in the lungs should be carefully examined as it helps in the diagnostic process during the pandemic period. For this reason, the first diagnosis and medications are very important to prevent the disease from progressing. In this study, a dataset consisting of Pneumonia and Normal images was used by proposing a new image preprocessing process. These preprocessed images were reduced to 15x15 unit size and their features were extracted according to their RGB values. Experimental studies were carried out by performing both normal values and feature reduction among these features. RGB values of the images were used in train and test processes for MLAs. In experimental studies, 5 different Machine Learning Algorithms (MLAs) (Multi Class Support Vector Machine (MC-SVM), k Nearest Neighbor (k-NN), Decision Tree (DT), Multinominal Logistic Regression (MLR), Naive Bayes (NB)). The following accuracy rates were obtained in train operations for MLAs, respectively; 1, 1, 1, 0.746377, 0.963768. Accuracy results in test operations were obtained as follows; 0.87755, 0.857143, 0.857143, 0.877551, 0.938776. [ABSTRACT FROM AUTHOR]

Using a powder scintillator for a detection of gamma and X-rays allows to create detectors of various shapes, moreover the handling and fabrication of such scintillators is much easier than in the case of large or thin monocrystals. We report on the application of YAlO 3 :Ce powder to Mössbauer spectroscopy. The detection efficiency and the energy resolution of the scintillators made of powders of two different granularities (grain size ≤ 30 μ m and 50– 70 μ m) were investigated. The powder scintillators exhibited a comparable energy resolution and detection efficiency as YAlO 3 :Ce monocrystal with thickness of 400 μ m. Burying the YAlO 3 :Ce grains into the epoxy resin increased the energy resolution of powder scintillators. The studied scintillators were found to be suitable for experiments in Mössbauer spectroscopy. The performance of YAP is also compared to the NaI:Tl scintillator. [Display omitted] • Yap:Ce powder exhibits comparable detection efficiency as YAP:Ce monocrystal. • Covering YAP:Ce powder with epoxy resin increases its energy resolution and mechanical hardness. • YAP:Ce powder is efficient for a photon detection in Mössbauer spectroscopy. • Scintillation crystals of various shapes could be made from YAP:Ce powder. [ABSTRACT FROM AUTHOR]

• A deep learning method is proposed using different optimizers and transfer learning to classify Pneumonia patients. • Preparing a dataset of around 5300 X-ray images for pneumonia detection. • The proposed deep transfer learning method is trained on a benchmark open dataset of chest x-ray images. • Presenting the optimization results, precision, recall, accuracy, and F1-score for proposed method. • In proposed method achieve better accuracy in the detection rate than other techniques. Pneumonia is a disease that leads to the death of individuals within a short period since the flow of fluid in the lungs. Hence, initial diagnosis and drugs are very important to avoid the progress of the disease. This paper proposes a novel deep learning approach for automatic detection of pneumonia using deep transfer learning to simplify the detection process with improved accuracy. This work was aimed to preprocess the input chest X-ray images to identify the presence of pneumonia using U-Net architecture based segmentation and classifies the pneumonia as normal and abnormal (Bacteria, viral) using pre-trained on ImageNet dataset models such as ResNet50, InceptionV3, InceptionResNetV2. Besides, to extract the efficient features and improve accuracy of pre-trained models two optimizers, namely, Adam and Stochastic Gradient Descent (SGD) used and its performances are analyzed with batch sizes of 16 and 32. Based on the values obtained, the performances of undertaken pre-trained models are analyzed and compared with other Convolutional Neural Network (CNN) models such as DenseNet-169+SVM, VGG16, RetinaNet + Mask RCNN, VGG16 and Xception, Fully connected RCNN, etc using various measures. From the results observed that the proposed ResNet50 model work achieved 93.06% accuracy, 88.97 % precision rate, 96.78% Recall rate and 92.71% F1-score rate, which than is higher than the other models aforementioned. [ABSTRACT FROM AUTHOR]

• A neural network is proposed for an edge detection model of noised X-ray images. • An image processing filter is embedded in the model for advanced detection. • X-ray image datasets with three levels of noise are built for training and test. • Defect image datasets are built in experiment stage for non-destructive testing. • The developed model presents superior noise resistance for defect detection. X-ray non-destructive testing (NDT) is a primary detection technology in industrial fields, providing an effective detection for fragile and complex structures without destructing components. In this study, we adopt the principle of convolutional neural network (CNN) and a Laplacian filter to propose an edge detection model with improved performance. By constructing X-ray image datasets with different noise levels, our proposed CNN model successfully detects fuzzy defects on noised X-ray images, and presents better structure similarity of the detected information compared to conventional edge detection algorithms, Canny and SUSAN. Additionally, the experiment results indicate that the noised training datasets effectively improves the model's capability of noise resistance in edge detection tasks. Furthermore, the quality of training images significantly affects the performance of the trained model. This study develops a robust edge detection algorithm for low-cost and noise-independent X-ray non-destructive testing technology, providing a meaningful reference in edge detection of industrial X-ray images. [ABSTRACT FROM AUTHOR]

• The PDD curves of capped and uncapped fiber X-ray sensors are almost opposite. • A theoretical model was established to explain the different trend of PDD curves. • The peak or valley of the curves of Cerenkov radiation are at a depth of 1.5 cm. The use of optical fiber X-ray sensors is of great significance in X-ray detection. To reduce the influence of background noise attributed to sources such as Cerenkov radiation, the response of a plain (no scintillator material added) optical fiber to the X-ray radiation is investigated. The percentage dose-depth (PDD) of plain optical fibers with and without visible light blocking end caps are calculated and are clearly different. The leakage of Cerenkov radiation and presence of Compton scattering from water into the sensing probe is considered as the reason for the difference. A theoretical model to explain the difference is proposed. The experimental results fit well to the theoretical expectation. [ABSTRACT FROM AUTHOR]

Aluminum alloy castings have a high utilization rate in the automotive industry, and its quality directly affects the safety performance of the mechanical components. Hence, casting quality management is vital during the casting production process. This paper presents a weakly-supervised Convolutional Neural Network model to recognize defects based on casting X-ray images. These images are divided into two classes including defective and non-defective. Firstly, attention maps are generated to represent the defective parts by weakly-supervised learning for each image. Then mutual-channel loss combined with the cross-entropy loss function encourage the network to focus on discriminative features. Simultaneously, a novel data-augmentation methods guided by these attention maps is proposed to enlarge the dataset. The test accuracy achieves 95.5%, and the recall is 96.0%, which means our model is accurate and robust. The efficiency of the proposed approach is verified by comparing the state-of-art approaches and the ablation experiments. • A weakly supervised convolutional neural network for classification is proposed. • The defective castings with the X-ray casting images are classified automatically. • The attention-guided data augmentation enlarges the dataset effectively. • The mutual-channel loss focuses on subtle discriminative details in two classes. [ABSTRACT FROM AUTHOR]

• Deep learning-based pneumonia detection in x-ray images is done in this work. • Different models of deep learning and transfer learning are analysed in this work for the image classification application. • An extensive analysis is carried out in this work with several experimental results. A large number of children die due to pneumonia every year worldwide. An estimated 1.2 million episodes of pneumonia were reported in children up to 5 years of age, of which 880,000 died in 2016. Hence, pneumonia is a major cause of death amongst children, with high prevalence rate in South Asia and Sub-Saharan Africa. Even in a developed country like the United States, pneumonia is among the top 10 causes of deaths. Early detection and treatment of pneumonia can reduce mortality rates among children significantly in countries having a high prevalence. Hence, this paper presents Convolutional Neural Network models to detect pneumonia using x-ray images. Several Convolutional Neural Networks were trained to classify x-ray images into two classes viz., pneumonia and non-pneumonia, by changing various parameters, hyperparameters and number of convolutional layers. Six models have been mentioned in the paper. First and second models consist of two and three convolutional layers, respectively. The other four models are pre-trained models, which are VGG16, VGG19, ResNet50, and Inception-v3. The first and second models achieve a validation accuracy of 85.26% and 92.31% respectively. The accuracy of VGG16, VGG19, ResNet50 and Inception-v3 are 87.28%, 88.46%, 77.56% and 70.99% respectively. [ABSTRACT FROM AUTHOR]