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This article explore lung cancer using a hybrid deep learning (DL) model. Thus, this article offers a hybrid CNN along with the SVM classification method with tuned hyperparameters, which is the orthogonal Convolution neural network-support vector machine OCNN-SVM model. Each lung image may be automatically categorized and analyzed by this system to determine whether cancer cells are present. CNN has fewer parameters and is easier to train than a fully linked network with an exact count of hidden units. SVM has also been used to remove irrelevant data that has an adverse effect on accuracy. The effectiveness of this method is assessed in this research, and the findings show that the proposed orthogonal Convolution neural network-support vector machine (OCNN-SVM) model has been successful in categorizing lung images. [ABSTRACT FROM AUTHOR]

In this research work, a new deep learning model named VGG-COVIDNet has been proposed which can classify COVID-19 cases from normal cases over X-Rays and CT scan images of lungs. Medical practitioners use the X-Rays and CT scan images of lungs to identify whether a person is infected from COVID or not. In present times, it is very important to give real time COVID prediction with high reliability of results. Deep learning models equipped with machine learning support have been found very influential in accurate prediction of COVID or Non-COVID cases in real time. However, there are some limitations associated with the performance of these model which are model size, achieving good balance of model size and accuracy, and making a single model fitting well for both X-Ray and CT Scan image datasets. Keeping in mind these performance constraints, this new model (VGG-COVIDNet) has been proposed for real time prediction of COVID cases with good balance of model size and accuracy working well for both type of datasets (CT Scan and X-Ray). In order to control model size, an improved version of VGG-16 architecture has been proposed which contains only 13 convolutional layers and 5 fully connected layers. Multiple dropout layers have been added in the proposed architecture which can drop some percentage of features and applies random transformations to decrease the model over-fitting issue. Keeping in mind the primary goal to increase the model accuracy the proposed model has been trained on different datasets with ReLU activation function which is one of the best non-linear activation functions. Four different capacity datasets with CT scan and X-Ray images have been used to validate the performance of proposed model. The proposed model gives an overall accuracy of more than 90% on both types of input datasets i.e. X-Ray and CT Scan. [ABSTRACT FROM AUTHOR]

• Novel method enabling tracking of obscured lung tumor in X-ray. • Validation on real-world patient data (CyberKnife 1 View patients). • Zero-shot deep learning framework leveraging deep learning infrastructure yet independent of specific data or patient cohort. Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available – fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., <15 mm) and/or tumors obscured by overlapping anatomies. To markedly improve the performance of fiducial-free tracking, we proposed a deep learning-based template matching algorithm – Deep Match. Deep Match consists of four self-definable stages – training-free feature extractor, similarity measurements for location proposal, local refinements, and uncertainty level prediction for constructing a more trustworthy and versatile pipeline. Deep Match was validated on a 10 (38 fractions; 2661 images) patient cohort whose lung tumor was trackable on one X-ray view, while the second view did not offer sufficient conspicuity for tumor tracking using existing methods. The patient cohort was stratified into subgroups based on tumor sizes (<10 mm, 10–15 mm, and >15 mm) and tumor locations (with/without thoracic anatomy overlapping). On X-ray views that conventional methods failed to track the lung tumor, Deep Match achieved robust performance as evidenced by >80 % 3 mm-Hit (detection within 3 mm superior/inferior margin from ground truth) for 70 % of patients and <3 mm superior/inferior distance (SID) ∼1 mm standard deviation for all the patients. Deep Match is a zero-shot learning network that explores the intrinsic neural network benefits without training on patient data. With Deep Match, fiducial-free tracking can be extended to more patients with small tumors and with tumors obscured by overlapping anatomy. [ABSTRACT FROM AUTHOR]

The Aluminium Conductor Composite Core (ACCC) is a critical component of power transmission that needs automated nondestructive defect detection to prevent wire breaking accidents and ensure regular city operations. Unfortunately, the current supervised target detection scheme is limited in its generalization ability due to the diversity of defect morphology and the high cost of acquiring samples. To overcome these challenges, we propose an automatic defect detection method based on a modified Skip-GANomaly model using X-ray images of ACCC wires as input. We experiment with semi-supervised deep learning reconstruction methods to mitigate the lack of abnormal samples and improve the generalization ability of unknown defects. We also explore hierarchical reconstruction and Skip-Connection adjustment to enhance the reconstruction effect and defect identification based on Resnet50. The results show that our method can achieve high accuracy, recall, and precision levels of over 99 %, significantly highlighting the difference in defect morphology. Compared to existing supervised schemes, our Defect Detection Scheme with Hierarchical Reconstruction and Anomaly-Subtracted Image Discrimination (DHRAD) eliminates dependence on the number of abnormal samples and the diversity of defect types in the training set, and has the ability to detect defects with unknown morphology. Therefore, DHRAD provides an effective detection scheme for automatic nondestructive defect detection of ACCC in operation, based on computer vision and semi-supervised learning. [ABSTRACT FROM AUTHOR]

Automatic and rapid screening of COVID-19 from the radiological (X-ray or CT scan) images has become an urgent need in the current pandemic situation of SARS-CoV-2 worldwide. However, accurate and reliable screening of patients is challenging due to the discrepancy between the radiological images of COVID-19 and other viral pneumonia. So, in this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray and CT images. In the proposed approach, different sub-models have been obtained from the VGG19 and the Xception models during the training. Thereafter, obtained sub-models are stacked together using softmax classifier. The proposed stacked CNN model combines the discriminating power of the different CNN's sub-models and detects COVID-19 from the radiological images. In addition, we collect CT images to build a CT image dataset and also generate an X-ray images dataset by combining X-ray images from the three publicly available data repositories. The proposed stacked CNN model achieves a sensitivity of 97.62% for the multi-class classification of X-ray images into COVID-19, Normal and Pneumonia Classes and 98.31% sensitivity for binary classification of CT images into COVID-19 and no-Finding classes. Our proposed approach shows superiority over the existing methods for the detection of the COVID-19 cases from the X-ray radiological images. [ABSTRACT FROM AUTHOR]

Waste management plays an important role in environmental protection and resource recycling. In fact, traditional waste recycling requires a large number of workers to sort manually, which is not only labor-intensive and time-consuming, but also increases the health risk for workers. To further improve the efficiency of waste detection and classification, related scholars have used computer vision technology to achieve automatic waste management. However, existing methods tend to use RGB images as input, requiring workers to open waste bags and disperse waste items in advance. To avoid comprehensive preparation procedures and identify occluded waste items, we use deep learning models to detect and recognize waste items in X-ray images. Compared to RGB images, X-ray images can effectively preserve boundary information and reflect material types. To take advantage of the inherent characteristics of X-ray images, we propose a novel X-ray Characteristic Enhancement Module (XCEM) to improve the performance of X-ray waste detection. XCEM consists of three sub-modules, namely Hybrid Interaction Module (HIM), Multi-scale Material Perception Module (MMPM), and Boundary Guidance Module (BGM). Specifically, HIM not only generates soft and hard boundary maps, but also achieves information interaction and feature enhancement of different boundary maps. Meanwhile, MMPM can effectively capture different multi-scale material features. In addition, BGM uses auxiliary boundary information and attention mechanisms to effectively enhance critical material features. As a plug-and-play module, XCEM can be readily inserted into the most popular detection pipelines. We select several representative one-stage and two-stage methods and perform related experiments on the WIXRay dataset. Experimental results show that our method can achieve consistent performance gains on diverse detection paradigms. This study promotes the development of intelligent waste inspection and recycling. • An efficient solution based on X-ray images for waste detection and classification. • The proposed XCEM can make use of the inherent characteristics of X-ray images. • The XCEM can achieve consistent performance gains on diverse detection models. • Several representative methods are explored for waste detection in X-ray images. • Using computer vision and deep learning can achieve automatic waste management. [ABSTRACT FROM AUTHOR]

• A light weight CORONA-NET for diagnosing COVID-19 from chest X-ray images. • Deep feature extraction is performed by CNN, reduced yet strengthened by DWT. • The dataset included 3000 X-rays obtained locally through hospital in Denmark. • The method achieves excellent performance in comparison with the existing methods. • The proposed system has a 99.56 percent accuracy and 99.667 percent specificity. Since December 2019, COVID-19 has posed the most serious threat to living beings. With the advancement of vaccination programs around the globe, the need to quickly diagnose COVID-19 in general with little logistics is fore important. As a consequence, the fastest diagnostic option to stop COVID-19 from spreading, especially among senior patients, should be the development of an automated detection system. This study aims to provide a lightweight deep learning method that incorporates a convolutional neural network (CNN), discrete wavelet transform (DWT), and a long short-term memory (LSTM), called CORONA-NET for diagnosing COVID-19 from chest X-ray images. In this system, deep feature extraction is performed by CNN, the feature vector is reduced yet strengthened by DWT, and the extracted feature is detected by LSTM for prediction. The dataset included 3000 X-rays, 1000 of which were COVID-19 obtained locally. Within minutes of the test, the proposed test platform's prototype can accurately detect COVID-19 patients. The proposed method achieves state-of-the-art performance in comparison with the existing deep learning methods. We hope that the suggested method will hasten clinical diagnosis and may be used for patients in remote areas where clinical labs are not easily accessible due to a lack of resources, location, or other factors. [ABSTRACT FROM AUTHOR]

• In this research, we introduced a novel TB-UNet based on dilated fusion block (DF) and Attention gate (AG) mechanism for accurate segmentation of lung regions. • We also proposed TB-DenseNet, which is based on five dual convolution blocks, DenseNet-169 layer, and a feature fusion block for the precise classification of tuberculosis images. • The experiments have been performed on three chest X-ray (CXR) datasets, segmented images, and original images are fed to TB-DenseNet for better classification. Tuberculosis still significantly impacts the world's population, with more than 10 million people getting sick each year. Researchers have focused on developing computer-aided diagnosis (CAD) technology based on X-ray imaging to support the identification of tuberculosis, and deep learning is a popular and efficient method. However, deep learning-based CAD approaches usually ignore the relationship between the two vision tasks of specific region segmentation and classification. In this research, we introduced a novel TB-UNet, which is based on dilated fusion block (DF) and Attention block (AB) block for accurate segmentation of lungs regions and achieved the highest results in terms of Precision (0.9574), Recall (0.9512), and F1score (0.8988), IoU (0.8168) and Accuracy (0.9770). We also proposed TB-DenseNet which is based on five dual convolution blocks, DenseNet-169 layer, and a feature fusion block for the precise classification of tuberculosis images. The experiments have been performed on three chest X-ray (CXR) datasets, segmented images, and original images are fed to TB-DenseNet for better classification. Furthermore, the proposed method is tested against simultaneously three different diseases, such as Pneumonia, COVID-19, and Tuberculous. The highest results are achieved in terms of Precision (0.9567), Recall (0.9510), F1score (0.9538), and Accuracy (0.9510). The achieved results reflect that our proposed method produces the highest accuracy compared to the state-of-the-art methods. The source code is available at: https://github.com/ahmedeqbal/TB-DenseNet. [ABSTRACT FROM AUTHOR]

Landmark detection has been well developed by deep learning methods, and the cascade-connected neural network (CCNN) stands out as a widely used deep learning landmark detection method. CCNNs consist of several stacked network backbones, where the predictions of the previous network backbone are used as the input of the following one. Due to GPU memory bottlenecks, CCNNs have two limitations. First, the network backbones of CCNNs have limited numbers and depths; thus, the learning ability of CCNNs is limited. Second, CCNNs are usually trained in low-resolution images. However, the neighboring pixels in high-resolution images are usually vital for landmark detection, especially for cephalometric landmark detection. This paper interprets CCNNs as the discrete approximation of ordinary differential equations. Relying on this explanation, we further propose a novel model, called the cascade-refine model , which takes advantage of CCNNs and makes it possible to overcome the limitations of number and depth by sharing parameters among stacked network backbones. Moreover, the proposed model obeys the rule of coarse-to-fine architectures, where a global module is used to generate the coarse landmark locations, and a local module is adopted to tune the pixel error of latent landmark locations in the region of interest. The proposed model is trained in an end-to-end manner. Thus, the neighboring detailed high-resolution pixels are directly exploited for cephalometric landmark detection. The proposed cascade-refine model outperforms state-of-the-art methods on the public Automatic Cephalometric X-ray Landmark Detection Challenge 2015 dataset. We also built two private cephalometric X-ray datasets, and experimental results on both datasets demonstrate the good performance of the proposed model. • A cascade training strategy was introduced to model the discrete approximation of an ODE. • The proposed cascade-refine model can continually refine landmark locations. • The cascade-refine model breaks through the limitation of GPU memory. • The landmark label map was proposed to the local module of the cascade-refine model. [ABSTRACT FROM AUTHOR]

The application of deep learning to X-ray image classification and recognition has attracted increasing attention in intelligent radiographic testing. In this study, we developed a defect detection method for X-ray images of aeroengine turbine blades using a novel unsupervised adversarial learning strategy. A defect detection model is proposed based on a generative adversarial network (GAN), which consists of a generator network, a discriminator network, and an extra encoder network. Employing an encoder–decoder–encoder subnetwork in the generator network enables the model to map an input image to a low-dimensional encoding vector, which is subsequently used to reconstruct the input image. The extra encoder network mapped the reconstructed image to a new encoding vector. Model training is implemented with non-defective X-ray image samples by minimising the distance between the original input image and the generated image (reconstruction loss) and the encoding vector distance (encoding loss). Defect detection is achieved using a generator network that can reconstruct non-defect samples but cannot reconstruct those with defects well. The effects of input image size, number of encoding nodes, and type of reconstruction loss on the performance of the defect detection model were studied. Consequently, the proposed model with an input image size of 128 × 128, 600 encoding nodes, and L 2 reconstruction loss can achieve a detection accuracy of 0.911. This study proposes a method for developing intelligent radiographic testing techniques for the quality management of aeroengines. [ABSTRACT FROM AUTHOR]

Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR images. However, it requires large-scale and diverse medical data with high-quality annotations to train such robust and accurate CADs. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation. Nevertheless, previous methods lack the ability to generate nodules that are realistic with the shape/size attributes desired by the detector. To address this issue, we introduce a novel lung nodule synthesis framework in this paper, which decomposes nodule attributes into three main aspects including the shape, the size, and the texture, respectively. A GAN-based Shape Generator firstly models nodule shapes by generating diverse shape masks. The following Size Modulation then enables quantitative control on the diameters of the generated nodule shapes in pixel-level granularity. A coarse-to-fine gated convolutional Texture Generator finally synthesizes visually plausible nodule textures conditioned on the modulated shape masks. Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task. Our experiments demonstrate the enhanced image quality, diversity, and controllability of the proposed lung nodule synthesis framework. We also validate the effectiveness of our data augmentation strategy on greatly improving nodule detection performance. • Lung nodule synthesis framework disentangles nodule attributes to the shape, the size, and the texture, respectively. • Applying modulated shape mask provides diverse variation of nodule shape and quantitative control of nodule size. • Data augmentation based on the controllability of lung nodule synthesis effectively improves lung nodule detection. [ABSTRACT FROM AUTHOR]

Pneumothorax can be life-threatening if not diagnosed on time. Most of the research studies for pneumothorax detection in chest X-rays (CXR) images have applied the transfer learning approach. In this study, we have explored the parameter-efficient attention based lightweight convolutional neural network (ALCNN) for pneumothorax detection in CXR images. It has stacked convolutional layers with the attention mechanism to re-calibrate channel-wise feature maps. Furthermore, we have also evaluated three different transfer learning approaches to compare the performance with ALCNN. The ALCNN has obtained the comparable results with 10x fewer parameters in comparison to VGG-19 and ResNet-50 architectures. The ALCNN achieved a 0.73 sensitivity and an area under curve (AUC) of 0.79 on the held-out test set. While pre-trained and fine-tuned, VGG, 19, achieved a sensitivity value of 0.52 and 0.71 and an AUC value of 0.72 and 0.81, respectively. Similarly, pre-trained and fine-tuned ResNet-50 achieved a sensitivity value of 0.69 and 0.74 and an AUC value of 0.77 and 0.80, respectively. In the third transfer learning approach, VGG, 19, and ResNet-50 obtained sensitivity values of 0.65 and 0.69, respectively, while the AUC value of 0.79 for both VGG-19 and ResNet-50. The results obtained in our study show that transfer learning did not have a significant impact on performance for pneumothorax detection in CXR. [Display omitted] • Parameter-efficient ALCNN model is proposed for pneumothorax detection in chest X-rays. • The ALCNN obtained comparable results with ten times less parameters when compared with three transfer learning approaches on two deep architectures. • Obtained results show that the transfer learning approaches did not have a significant effect on the performance for pneumothorax detection. [ABSTRACT FROM AUTHOR]

Although biopsy-based necrosis rate is a golden standard for reflecting the sensitivity of bone tumor and guiding postoperative chemotherapy, it requires biopsy which is invasive and time-consuming. In this paper, we develop a new necrosis rate detection method using time series X-ray images instead of biopsy. To overcome the limitations of few-shot samples, the proposed method utilizes a Generative Adversarial Network with Long Short-term Memory to generate time series X-ray images. For further data expansion, an image-to-image translation network is applied for producing the initial images. These augmented data are treated as the training set of a 3D-Convolutional Neural Network classification model. Our method expands the few-shot bone tumor X-rays by 10 times, and approaches the necrotic rate classification result of biopsy, which is the state-of-the-art technique in the detection of few-shot bone tumor necrosis rate. Furthermore, it provides an efficient method to investigate the bone tumor necrosis rate in few-shot samples. • A deep learning method is proposed to detect necrosis rate with few-shot X-rays. • The Conv-LSTM-GAN network in our method is designed to generate bone tumor images. • Our method expands bone tumor images by 10 times and achieves biopsy-like results. [ABSTRACT FROM AUTHOR]

Object detection algorithms based on deep learning have been widely used in the intelligent detection of prohibited items in X-ray images during the security inspection process. However, security inspection is a process that emphasizes timeliness, and the low inference efficiency brought by overly complex deep neural networks is not allowed. In this paper, we propose a lightweight object detection framework based on the YOLOv4 algorithm, named LightRay. Where MobileNetV3 is applied as the feature extraction backbone. To effectively deal with the detection problem of prohibited items with small sizes in complex backgrounds, a feature enhancement network in shallow layers is proposed. Where, the Lightweight Feature Pyramid Network (LFPN) and the Convolutional Block Attention Module (CBAM) effectively achieve feature fusion of different scales, while strengthening the features of small objects. The experimental results show that the mAP of the lightRay model is 87.28% on the SIXray data set, while the FLOPs of the model are reduced to the original 1/5, and Params is reduced to 1/3 of the original. In addition, some ablation experiments confirm the ability of the LightRay model the detection of prohibited items with small sizes. The codes of LightRay are released at https://github.com/zhg-SZPT/LightRay. [ABSTRACT FROM AUTHOR]

The novel COVID-19 pandemic, has effectively turned out to be one of the deadliest events in modern history, with unprecedented loss of human life, major economic and financial setbacks and has set the entire world back quite a few decades. However, detection of the COVID-19 virus has become increasingly difficult due to the mutating nature of the virus, and the rise in asymptomatic cases. To counteract this and contribute to the research efforts for a more accurate screening of COVID-19, we have planned this work. Here, we have proposed an ensemble methodology for deep learning models to solve the task of COVID-19 detection from chest X-rays (CXRs) to assist Computer-Aided Detection (CADe) for medical practitioners. We leverage the strategy of transfer learning for Convolutional Neural Networks (CNNs), widely adopted in recent literature, and further propose an efficient ensemble network for their combination. The DenseNet-201 architecture has been trained only once to generate multiple snapshots, offering diverse information about the extracted features from CXRs. We follow the strategy of decision-level fusion to combine the decision scores using the blending algorithm through a Random Forest (RF) meta-learner. Experimental results confirm the efficacy of the proposed ensemble method, as shown through impressive results upon two open access COVID-19 CXR datasets — the largest COVID-X dataset, as well as a smaller scale dataset. On the large COVID-X dataset, the proposed model has achieved an accuracy score of 94.55% and on the smaller dataset by Chowdhury et al., the proposed model has achieved a 98.13% accuracy score. • Proposed a CNN ensemble framework for medical practitioners. • A COVID-19 Detection system for Chest X-ray images is established. • Generated base model snapshots for ensembling by training the CNN model. • Followed decision-level fusion to combine decision scores using blending algorithm. • Appreciable results on largest COVID-X public dataset verifies the efficacy of the method. [ABSTRACT FROM AUTHOR]

• An image preprocessing method is applied to CXR data, and the segmented images are selected and fed into the model. • Introduction of fine-tuned Global Attention Blocks (GAB) and Category Attention Blocks (CAB) to obtain more detailed information on micro lesions. • The DenseNet and VGG models were fused to detect different pneumonia diseases. Since December 2019, the novel coronavirus disease (COVID-19) caused by the syndrome coronavirus 2 (SARS-CoV-2) strain has spread widely around the world and has become a serious global public health problem. For this high-speed infectious disease, the application of X-ray to chest diagnosis plays a key role. In this study, we propose a chest X-ray image classification method based on feature fusion of a dense convolutional network (DenseNet) and a visual geometry group network (VGG16). This paper adds an attention mechanism (global attention machine block and category attention block) to the model to extract deep features. A residual network (ResNet) is used to segment effective image information to quickly achieve accurate classification. The average accuracy of our model in detecting binary classification can reach 98.0%. The average accuracy for three category classification can reach 97.3%. The experimental results show that the proposed model has good results in this work. Therefore, the use of deep learning and feature fusion technology in the classification of chest X-ray images can become an auxiliary tool for clinicians and radiologists. [ABSTRACT FROM AUTHOR]

• A cascade framework is proposed for automatic lung segmentation in CXRs. • An adaptive closed polyline searching method is used to obtain data sequence. • An improved machine learning model is proposed to express a mathematical model. • The explainability-guided mathematical model is used to denote lung contour. • Performance of the proposed method is evaluated on public multi-site datasets. Large variations in anatomical shape and size, too much overlap between anatomical structures, and inconsistent anatomical shapes make accurate lung segmentation in chest x-rays (CXR) a challenging problem. In this paper, we propose an automatic method called A-LugSeg that consists of two subnetworks for lung segmentation in CXRs. The first is a segmentation subnetwork based on a deep learning model (i.e., Mask-RCNN), which completes a coarse segmentation for each input CXR image. The second is a refinement subnetwork designed to optimize the coarse segmentation result by combining an improved closed principal curve method and an enhanced machine learning, where the lung contour's explainability-guided mathematical model is expressed by the machine learning's parameters. The performance of the proposed method is evaluated on three public datasets, namely the ShenZhen hospital Chest X-ray dataset (SZCX), Japanese Society of Radiological Technology dataset (JSRT), and Montgomery County chest x-ray dataset (MC), which contain the 662 CXRs, 247 CXRs, and 138 CXRs, respectively. We used different datasets for training/validation (SZCX) and testing (SZCX/JSRT/MC). Furthermore, we used six evaluation metrics to evaluate the performance of our proposed method, including Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (Ω), Accuracy (ACC), Precision, Sensitivity, and Specificity. The obtained results (DSC = 0.973, Ω = 0.958, ACC = 0.972, and p -value for DSC < 0.001) for JSRT, (DSC = 0.971, Ω = 0.955, ACC = 0.97, and p -value for DSC < 0.001) for MC, (DSC = 0.972, Ω = 0.956, and ACC = 0.97) for hybrid datasets (JSRT + MC), and (Precision, Sensitivity, and Specificity are higher than 0.98) show the superior performance of the proposed dual subnetwork segmentation algorithm compared to the existing state of the art approaches. [ABSTRACT FROM AUTHOR]

• A dataset of X-ray radiographs of pear fruit was simulated from CT scans. • Deep anomaly detection models were trained on data of healthy pears and tested on data of healthy and defect pears. • Synthetic anomalies in training improved performance over the unsupervised case. • High AUC scores (up to 0.96) and classification accuracy (up to 95%) were obtained. • Defect pears with a cavity percentage > 1.0% could be detected 100% accurate. To preserve the quality of fresh fruit after harvest and to meet the year-round demand for high-quality fruit, pears are stored under a controlled atmosphere. However, due to preharvest events or suboptimal storage conditions, internal disorders might develop resulting in severe quality loss. Examples include internal browning and cavities, which are invisible externally. Here, X-ray radiography is investigated as a technique for internal quality inspection. The detection of defect fruit is approached as an anomaly detection (AD) problem, in which a model is constructed using nominal data and an anomaly score is used to identify defect fruit. In this work, multiple deep AD methods are shown to be effective to detect pears with internal cavity and browning disorders using X-ray radiographs (mean area under the receiver operating characteristic curve (AUC) up to 0.962). The best performing methods were found on par with a state-of-the-art multisensor disorder detection method (mean AUC up to 0.966). By investigating AD performance in function of internal disorder severity, it was shown that defect fruit with a cavity volume percentage > 1.0% could be detected 100% accurate using inline X-ray imaging. For lower cavity area percentages, the accuracy depended on the internal browning severity. Additionally, the explainability of the deep AD methods, i.e., how well human interpretable insight can be provided from each method's predictions, were qualitatively evaluated using anomaly heatmaps, which provided useful insight in the execution of the deep learning algorithms. [ABSTRACT FROM AUTHOR]

• Intracranial vessel perforation is a serious complication in EVT, procedural success may benefit from fast automated detection. • Automatic intracranial vessel perforation detection in DSA is studied for the first time. • Perforations are pooled from multiple (inter-)national clinical trials/registries. • Learning perforation progression characteristics as well as ensemble modeling improves performance. • The methods reach human expert level performance in perforation detection. [Display omitted] Intracranial vessel perforation is a peri-procedural complication during endovascular therapy (EVT). Prompt recognition is important as its occurrence is strongly associated with unfavorable treatment outcomes. However, perforations can be hard to detect because they are rare, can be subtle, and the interventionalist is working under time pressure and focused on treatment of vessel occlusions. Automatic detection holds potential to improve rapid identification of intracranial vessel perforation. In this work, we present the first study on automated perforation detection and localization on X-ray digital subtraction angiography (DSA) image series. We adapt several state-of-the-art single-frame detectors and further propose temporal modules to learn the progressive dynamics of contrast extravasation. Application-tailored loss function and post-processing techniques are designed. We train and validate various automated methods using two national multi-center datasets (i.e., MR CLEAN Registry and MR CLEAN-NoIV Trial), and one international multi-trial dataset (i.e., the HERMES collaboration). With ten-fold cross-validation, the proposed methods achieve an area under the curve (AUC) of the receiver operating characteristic of 0.93 in terms of series level perforation classification. Perforation localization precision and recall reach 0.83 and 0.70 respectively. Furthermore, we demonstrate that the proposed automatic solutions perform at similar level as an expert radiologist. [ABSTRACT FROM AUTHOR]

• An improved deep transfer learning model is proposed for spacecraft composite structures. • Our method can detect inclusion and void defects. It still applies if there are enough samples of other types of defects. • Small-size defects can be detected using the proposed method. • It is suitable for the small-scale sample dataset. This is important in the application. Usually, defect detection in X-ray images of composite materials is mainly performed by humans, which is time consuming and inefficient. Some deep learning methods have been proposed, but they are only used to identify a certain type of defect or need a large number of training samples. Besides, the size of void and inclusion defects in X-ray images of spacecraft composite structures (SCS) is very small, which is difficult for common object detection methods, especially when the number of training samples is small. Thus, we propose a transfer learning object detection model for detection of both of these defects. Based on domain adaptive Faster R-CNN (DA Faster), the feature pyramid network (FPN) is added into the feature extraction sub-module for multi-scale features adaptation, and small anchor strategies and ROI Align are utilized, which are helpful for localization of small-size defects. Besides, big NMS threshold and conditional domain adaptation (CDAN) is adopted in the box classification branch for more accurate features alignment. The experiment results demonstrate that strong ability of the proposed model in detecting small-size void and inclusion defects of SCS. [ABSTRACT FROM AUTHOR]

• We are achieving High Performance COVID-19 Detection compared to existing methods. • This method is less time consuming that RTPCR. The Coronavirus Disease 2019 (COVID-19) outbreak has a devastating impact on health and the economy globally, that's why it is critical to diagnose positive cases rapidly. Currently, the most effective test to detect COVID-19 is Reverse Transcription-polymerase chain reaction (RT-PCR) which is time-consuming, expensive and sometimes not accurate. It is found in many studies that, radiology seems promising by extracting features from X-rays. COVID-19 motivates the researchers to undergo the deep learning process to detect the COVID- 19 patient rapidly. This paper has classified the X-rays images into COVID- 19 and normal by using multi-model classification process. This multi-model classification incorporates Support Vector Machine (SVM) in the last layer of VGG16 Convolution network. For synchronization among VGG16 and SVM we have added one more layer of convolution, pool, and dense between VGG16 and SVM. Further, for transformations and discovering the best result, we have used the Radial Basis function. CovXmlc is compared with five existing models using different parameters and metrics. The result shows that our proposed CovXmlc with minimal dataset reached accuracy up to 95% which is significantly higher than the existing ones. Similarly, it also performs better on other metrics such as recall, precision and f-score. [ABSTRACT FROM AUTHOR]

• A novel multi-kernel-size spatial-channel attention model for COVID-19 detection • The proposed method enables early diagnosis from chest X-ray images. • Evaluation with real-world data demonstrates improved accuracy at 98.2 Novel coronavirus 2019 (COVID-19) has spread rapidly around the world and is threatening the health and lives of people worldwide. Early detection of COVID-19 positive patients and timely isolation of the patients are essential to prevent its spread. Chest X-ray images of COVID-19 patients often show the characteristics of multifocality, bilateral hairy glass turbidity, patchy network turbidity, etc. It is crucial to design a method to automatically identify COVID-19 from chest X-ray images to help diagnosis and prognosis. Existing studies for the classification of COVID-19 rarely consider the role of attention mechanisms on the classification of chest X-ray images and fail to capture the cross-channel and cross-spatial interrelationships in multiple scopes. This paper proposes a multi-kernel-size spatial-channel attention method to detect COVID-19 from chest X-ray images. Our proposed method consists of three stages. The first stage is feature extraction. The second stage contains two parallel multi-kernel-size attention modules: multi-kernel-size spatial attention and multi-kernel-size channel attention. The two modules capture the cross-channel and cross-spatial interrelationships in multiple scopes using multiple 1D and 2D convolutional kernels of different sizes to obtain channel and spatial attention feature maps. The third stage is the classification module. We integrate the chest X-ray images from three public datasets: COVID-19 Chest X-ray Dataset Initiative, ActualMed COVID-19 Chest X-ray Dataset Initiative, and COVID-19 radiography database for evaluation. Experimental results demonstrate that the proposed method improves the performance of COVID-19 detection and achieves an accuracy of 98.2%. [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 key factor in the fight against viral diseases such as the coronavirus (COVID-19) is the identification of virus carriers as early and quickly as possible, in a cheap and efficient manner. The application of deep learning for image classification of chest X-ray images of COVID-19 patients could become a useful pre-diagnostic detection methodology. However, deep learning architectures require large labelled datasets. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. Moreover, in such context, the datasets are also highly imbalanced, with few observations from positive cases of the new disease. In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch with a very limited number of labelled observations and highly imbalanced labelled datasets. We demonstrate the critical impact of data imbalance to the model's accuracy. Therefore, we propose a simple approach for correcting data imbalance, by re-weighting each observation in the loss function, giving a higher weight to the observations corresponding to the under-represented class. For unlabelled observations, we use the pseudo and augmented labels calculated by MixMatch to choose the appropriate weight. The proposed method improved classification accuracy by up to 18%, with respect to the non balanced MixMatch algorithm. We tested our proposed approach with several available datasets using 10, 15 and 20 labelled observations, for binary classification (COVID-19 positive and normal cases). For multi-class classification (COVID-19 positive, pneumonia and normal cases), we tested 30, 50, 70 and 90 labelled observations. Additionally, a new dataset is included among the tested datasets, composed of chest X-ray images of Costa Rican adult patients. • COVID-19 detection deep learning architectures typically need many labels. • Also the datasets at the beginning of a virus outbreak are highly imbalanced. • Semi supervised data can be used to increase model's accuracy with few labels. • The effect of data imbalance on semi-supervised learning is under-explored. • A method to correct data imbalance for semi supervised learning is proposed. [ABSTRACT FROM AUTHOR]

The 2019 novel coronavirus (COVID-19) originating from China, has spread rapidly among people living in other countries. According to the World Health Organization (WHO), by the end of January, more than 104 million people have been affected by COVID-19, including more than 2 million deaths. The number of COVID-19 test kits available in hospitals is reduced due to the increase in regular cases. Therefore, an automatic detection system should be introduced as a fast, alternative diagnostic to prevent COVID-19 from spreading among humans. For this purpose, three different BiT models: DenseNet, InceptionV3, and Inception-ResNetV4 have been proposed in this analysis for the diagnosis of patients infected with coronavirus pneumonia using X-ray radiographs in the chest. These three models give and examine Receiver Operating Characteristic (ROC) analyses and uncertainty matrices, using 5-fold cross-validation. We have performed the simulations which have visualized that the pre-trained DenseNet model has the best classification efficiency with 92% among two other models proposed (83.47% accuracy for inception V3 and 85.57% accuracy for Inception-ResNetV4). • COVID-19 prediction using chest X-ray images. • The manual method i.e. PCR is time-consuming and high costs. • A novel automated image-based technique DenseNet and BiTModels like R101x1, which segment and identify Covid-19 patient x-ray. • Covid-19 normal and affected patient data is used. • Comparing with other models, training and testing accuracy, the R101x1 has an average accuracy, i.e., 91% in the Covid-19 class Prediction. [ABSTRACT FROM AUTHOR]

• A deep learning COVID-19 diagnostic system is developed using state-of-the-art deep learning architectures. • A detailed analysis of different methods used for detecting COVID-19 is presented in this paper. • Chest X-rays from different sources are collected to build a robust classification model. • The developed diagnostic model provided efficient results, even with a limited number of input images. • The results produced in this work are validated by an expert radiologist. In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. [ABSTRACT FROM AUTHOR]

• An deep learning system is proposed for automatic landmark detection in cephalometric X-ray images. • The proposed iterative method is used to fully extract and utilize the information associated with the landmarks in the images. • The transfer learning method between models is introduced, which allows the models between various types of landmarks to achieve the effect of shared learning and achieve mutual supplementation of information. • The system can effectively locate landmarks, providing considerable benefits in terms of reducing orthodontist workload and improving diagnostic accuracy and treatment efficiency. Cephalometric analysis is a commonly used method in modern orthodontic clinical diagnosis and treatment. The traditional way of manually marking cephalometric landmarks takes a long time, and it is difficult to obtain stable detection accuracy because of the uneven professionalism of doctors. In order to efficiently and accurately locate the landmarks of X-ray cephalograms, we propose a deep convolutional neural network (DCNN) system for automatic landmark detection in cephalometric X-ray images. And the system incorporates an iterative method while introducing the ideas of transfer learning and data enhancement. We first preprocess and expand the dataset of the cephalometric X-ray images. Then we use the preprocessed image to train a deep convolutional neural network and obtain preliminary prediction areas for various landmarks. Next, for each type of landmark, a detection process based on the iterative method is used to gradually reduce the prediction range in five iterations, which achieves more precise landmark positioning. Finally, the system restores the final predicted position in the original image by reversing the predicted coordinates. Our training set and test set proposed in the 2015 IEEE International Symposium on Biomedical Imaging each contain 150 cephalometric X-ray images. The accuracy of the system predicting 19 pairs of landmarks in the image within the error range of two millimeters reaches 87.51% on average. The experimental results and comparisons show that the iterative method does improve the accuracy and stability of location prediction and our cephalometric image landmark detector is effective in locating landmarks. [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]

• A novel deep transfer learning model is proposed for aeronautics composite materials. • An inspection method combining deep learning and sliding-window approach is explored. • It's the first application of transfer learning in composites' defect detection. Composite materials are increasingly used as structural components in military and civilian aircraft. To ensure their high reliability, numerous non-destructive testing (NDT) techniques have been used to detect defects during production and maintenance. However, most of these techniques are non-automatic, with diagnostic results determined subjectively by operators. Some deep learning methods have been proposed to identify defects in images obtained through NDT, but they need labeled image samples with defects, which can be expensive or unavailable. We propose a deep transfer learning model to accurately extract features for the inclusion of defects in X-ray images of aeronautics composite materials (ACM), whose samples are scarce. We researched an automatic inclusion defect detection method for X-ray images of ACM using our proposed model. Experimental results show that the model can reach 96% classification accuracy (F1_measure) with satisfactory detection results. [ABSTRACT FROM AUTHOR]

• We show how deep learning can be applied for COVID-19 detection from chest X-rays; • The proposed method is aimed to mark as first step a chest X-ray as related to a healthy patient or to a patient with pulmonary diseases, the second step is aimed to discriminate between generic pulmonary disease and COVID-19. The last step is aimed to detect the interesting area in the chest X-ray (to provide explainability); • We propose an explainable method aimed to automatically detect the areas of interest in the chest X-ray, symptomatic of the COVID-19 disease. • We obtain an accuracy of 0.99 in COVID-19 detection by evaluating 6,113 chest x-rays, with a time window required for the detection approximately equal to 2.5 seconds. Background and Objective : Coronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cases, pneumonia. The test to detect the presence of this virus in humans is performed on sputum or blood samples and the outcome is generally available within a few hours or, at most, days. Analysing biomedical imaging the patient shows signs of pneumonia. In this paper, with the aim of providing a fully automatic and faster diagnosis, we propose the adoption of deep learning for COVID-19 detection from X-rays. Method : In particular, we propose an approach composed by three phases: the first one to detect if in a chest X-ray there is the presence of a pneumonia. The second one to discern between COVID-19 and pneumonia. The last step is aimed to localise the areas in the X-ray symptomatic of the COVID-19 presence. Results and Conclusion : Experimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97. [ABSTRACT FROM AUTHOR]

• A new deep learning method is proposed and applied to detect arm fractures in X-rays. • The proposed method achieves the state-of-the-art average precision (AP) in arm fracture detection. • Experienced radiologists are invited to annotate nearly 4000 arm fracture X-rays to evaluate our method. In this paper, a novel deep learning method is proposed and applied to fracture detection in arm bone X-rays. The main improvements include three aspects. First, a new backbone network is established based on feature pyramid architecture to gain more fractural information. Second, an image preprocessing procedure including opening operation and pixel value transformation is developed to enhance the contrast of original images. Third, the receptive field adjustment containing anchor scale reduction and tiny RoIs expansion is exploited to find more fractures. In the experiments, nearly 4000 arm fracture X-ray radiographs collected from MURA dataset are annotated by experienced radiologists. The experiment results show that the proposed deep learning method achieves the state-of-the-art AP in arm fracture detection and it has strong potential application in real clinical environments. [ABSTRACT FROM AUTHOR]