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In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network that effectively amalgamates the strengths of EfficientNetB0 and DenseNet121, and it is enhanced by a suite of attention mechanisms for refined pneumonia image classification. Leveraging pre-trained models, our network employs multi-head, self-attention modules for meticulous feature extraction from X-ray images. The model's integration and processing efficiency are further augmented by a channel-attention-based feature fusion strategy, one that is complemented by a residual block and an attention-augmented feature enhancement and dynamic pooling strategy. Our used dataset, which comprises a comprehensive collection of chest X-ray images, represents both healthy individuals and those affected by pneumonia, and it serves as the foundation for this research. This study delves deep into the algorithms, architectural details, and operational intricacies of the proposed model. The empirical outcomes of our model are noteworthy, with an exceptional performance marked by an accuracy of 95.19%, a precision of 98.38%, a recall of 93.84%, an F1 score of 96.06%, a specificity of 97.43%, and an AUC of 0.9564 on the test dataset. These results not only affirm the model's high diagnostic accuracy, but also highlight its promising potential for real-world clinical deployment. [ABSTRACT FROM AUTHOR]

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network's learning rate to derive the optimal group's weight information and ultimately improve the model's detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time. [ABSTRACT FROM AUTHOR]

The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, however, non-trivial, prone to biases, nearly impossible for poor-quality data, and so far has been done manually by scientists. We present a novel machine-learning pipeline called Cavity Detection Tool (CADET), developed as an assistive tool that detects and estimates the sizes of X-ray cavities from raw Chandra images. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The convolutional network was trained using mock observations of early-type galaxies simulated to resemble real noisy Chandra -like images. The network's performance has been tested on simulated data obtaining an average cavity volume error of 14 per cent at an 89 per cent true-positive rate. For simulated images without any X-ray cavities inserted, we obtain a 5 per cent false-positive rate. When applied to real Chandra images, the pipeline recovered 93 out of 97 previously known X-ray cavities in nearby early-type galaxies and all 14 cavities in chosen galaxy clusters. Besides that, the CADET pipeline discovered seven new cavity pairs in atmospheres of early-type galaxies (IC 4765, NGC 533, NGC 2300, NGC 3091, NGC 4073, NGC 4125, and NGC 5129) and a number of potential cavity candidates. [ABSTRACT FROM AUTHOR]

In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is capable of accurate object detection, which provides a bounding box denoting the area in the X-rays that shows some possibility of TB (tuberculosis). The system makes use of CNNs (Convolutional Neural Networks) and YOLO models for the detection of the consolidation of cavitary patterns of the lesions and their detection, respectively. For this study, we experimented on the TBX11K dataset, which is a publicly available dataset. In our experiment, we employed class weights and data augmentation techniques to address the data imbalance present in the dataset. This technique shows a promising improvement in the model's performance and thus better generalization. In addition, it also shows that the developed model achieved promising results with a mAP (mean average precision) of 0.587, addressing class imbalance and yielding a robust performance for both obsolete pulmonary TB and active TB detection. Thus, our CAD system, rooted in state-of-the-art deep-learning and computer vision methodologies, not only advances diagnostic accuracy but also contributes to the mitigation of TB transmission risks. The substantial improvement in the model's performance and the ability to handle class imbalance underscore the potential of our approach for real-world TB detection applications. [ABSTRACT FROM AUTHOR]

Detecting detonators is a challenging task because they can be easily mis-classified as being a harmless organic mass, especially in high baggage throughput scenarios. Of particular interest is the focus on automated security X-ray analysis for detonators detection. The complex security scenarios require increasingly advanced combinations of computer-assisted vision. We propose an extensive set of experiments to evaluate the ability of Convolutional Neural Network (CNN) models to detect detonators, when the quality of the input images has been altered through manipulation. We leverage recent advances in the field of wavelet transforms and established CNN architectures—as both of these can be used for object detection. Various methods of image manipulation are used and further, the performance of detection is evaluated. Both raw X-ray images and manipulated images with the Contrast Limited Adaptive Histogram Equalization (CLAHE), wavelet transform-based methods and the mixed CLAHE RGB-wavelet method were analyzed. The results showed that a significant number of operations, such as: edges enhancements, altered color information or different frequency components provided by wavelet transforms, can be used to differentiate between almost similar features. It was found that the wavelet-based CNN achieved the higher detection performance. Overall, this performance illustrates the potential for a combined use of the manipulation methods and deep CNNs for airport security applications. [ABSTRACT FROM AUTHOR]

In order to improve the production quality and qualification rate of chips, X-ray nondestructive imaging technology has been widely used in the detection of chip defects, which represents an important part of the quality inspection of products after packaging. However, the current traditional defect detection algorithm cannot meet the demands of high accuracy, fast speed, and real-time chip defect detection in industrial production. Therefore, this paper proposes a new multi-scale feature fusion module (ATSPPF) based on convolutional neural networks, which can more fully extract semantic information at different scales. In addition, based on this module, we design a deep learning model (ATNet) for detecting lead defects in chips. The experimental results show that at 8.2 giga floating point operations (GFLOPs) and 146 frames per second (FPS), mAP0.5 and mAP0.5–0.95 can achieve an average accuracy of 99.4% and 69.3%, respectively, while the detection speed is faster than the baseline yolov5s by nearly 50%. [ABSTRACT FROM AUTHOR]

Objective: COVID‐19 is ravaging the world, but traditional reverse transcription‐polymerase reaction (RT‐PCR) tests are time‐consuming and have a high false‐negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID‐19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet. Methods: The proposed model integrates CNN and CapsNet. And attention mechanism module and multi‐branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal. Result: The test dataset includes 1200 X‐ray images (400 COVID‐19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception‐Resnet‐v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively. Conclusion: The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID‐19 X‐ray image dataset. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19), which emerged as a pandemic, has engulfed so many lives and affected millions of people across the world since December 2019. Although this disease is under control nowadays, yet it is still affecting people in many countries. The traditional way of diagnosis is time taking, less efficient, and has a low rate of detection of this disease. Therefore, there is a need for an automatic system that expedites the diagnosis process while retaining its performance and accuracy. Artificial intelligence (AI) technologies such as machine learning (ML) and deep learning (DL) potentially provide powerful solutions to address this problem. In this study, a state-of-the-art CNN model densely connected squeeze convolutional neural network (DCSCNN) has been developed for the classification of X-ray images of COVID-19, pneumonia, normal, and lung opacity patients. Data were collected from different sources. We applied different preprocessing techniques to enhance the quality of images so that our model could learn accurately and give optimal performance. Moreover, the attention regions and decisions of the AI model were visualized using the Grad-CAM and LIME methods. The DCSCNN combines the strength of the Dense and Squeeze networks. In our experiment, seven kinds of classification have been performed, in which six are binary classifications (COVID vs. normal, COVID vs. lung opacity, lung opacity vs. normal, COVID vs. pneumonia, pneumonia vs. lung opacity, pneumonia vs. normal) and one is multiclass classification (COVID vs. pneumonia vs. lung opacity vs. normal). The main contributions of this paper are as follows. First, the development of the DCSNN model which is capable of performing binary classification as well as multiclass classification with excellent classification accuracy. Second, to ensure trust, transparency, and explainability of the model, we applied two popular Explainable AI techniques (XAI). i.e., Grad-CAM and LIME. These techniques helped to address the black-box nature of the model while improving the trust, transparency, and explainability of the model. Our proposed DCSCNN model achieved an accuracy of 98.8% for the classification of COVID-19 vs normal, followed by COVID-19 vs. lung opacity: 98.2%, lung opacity vs. normal: 97.2%, COVID-19 vs. pneumonia: 96.4%, pneumonia vs. lung opacity: 95.8%, pneumonia vs. normal: 97.4%, and lastly for multiclass classification of all the four classes i.e., COVID vs. pneumonia vs. lung opacity vs. normal: 94.7%, respectively. The DCSCNN model provides excellent classification performance consequently, helping doctors to diagnose diseases quickly and efficiently. [ABSTRACT FROM AUTHOR]

Fracture is one of the most common and unexpected traumas. If not treated in time, it may cause serious consequences such as joint stiffness, traumatic arthritis, and nerve injury. Using computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection speed. However, there are still some problems in sternum fracture detection, such as the low detection rate of small and occult fractures. In this work, the authors have constructed a dataset with 1227 labelled X‐ray images for sternum fracture detection. The authors designed a fully automatic fracture detection model based on a deep convolution neural network (CNN). The authors used cascade R‐CNN, attention mechanism, and atrous convolution to optimise the detection of small fractures in a large X‐ray image with big local variations. The authors compared the detection results of YOLOv5 model, cascade R‐CNN and other state‐of‐the‐art models. The authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71, which is much better than using the YOLOv5 model (mAP = 0.44) and cascade R‐CNN (mAP = 0.55). [ABSTRACT FROM AUTHOR]

The technique for target detection based on a convolutional neural network has been widely implemented in the industry. However, the detection accuracy of X-ray images in security screening scenarios still requires improvement. This paper proposes a coupled multi-scale feature extraction and multi-scale attention architecture. We integrate this architecture into the Single Shot MultiBox Detector (SSD) algorithm and find that it can significantly improve the effectiveness of target detection. Firstly, ResNet is used as the backbone network to replace the original VGG network to improve the feature extraction capability of the convolutional neural network for images. Secondly, a multi-scale feature extraction (MSE) structure is designed to enrich the information contained in the multi-stage prediction feature layer. Finally, the multi-scale attention architecture (MSA) is fused onto the prediction feature layer to eliminate the redundant features' interference and extract effective contextual information. In addition, a combination of Adaptive-NMS and Soft-NMS is used to output the final prediction anchor boxes when performing non-maximum suppression. The results of the experiments show that the improved method improves the mean average precision (mAP) value by 7.4% compared to the original approach. New modules make detection much more accurate while keeping the detection speed the same. [ABSTRACT FROM AUTHOR]

Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods for predicting pneumonia cannot attain substantial levels of accuracy. This paper presents a computer-aided classification of pneumonia, coined Ensemble Learning (EL), to simplify the diagnosis process on chest X-ray images. Our proposal is based on Convolutional Neural Network (CNN) models, which are pretrained CNN models that have been recently employed to enhance the performance of many medical tasks instead of training CNN models from scratch. We propose to use three well-known CNNs (DenseNet169, MobileNetV2, and Vision Transformer) pretrained using the ImageNet database. These models are trained on the chest X-ray data set using fine-tuning. Finally, the results are obtained by combining the extracted features from these three models during the experimental phase. The proposed EL approach outperforms other existing state-of-the-art methods and obtains an accuracy of 93.91% and a F1-score of 93.88% on the testing phase. [ABSTRACT FROM AUTHOR]

The rapid spread of COVID-19 across the globe since its emergence has pushed many countries' healthcare systems to the verge of collapse. To restrict the spread of the disease and lessen the ongoing cost on the healthcare system, it is critical to appropriately identify COVID-19-positive individuals and isolate them as soon as possible. The primary COVID-19 screening test, RT-PCR, although accurate and reliable, has a long turn-around time. More recently, various researchers have demonstrated the use of deep learning approaches on chest X-ray (CXR) for COVID-19 detection. However, existing Deep Convolutional Neural Network (CNN) methods fail to capture the global context due to their inherent image-specific inductive bias. In this article, we investigated the use of vision transformers (ViT) for detecting COVID-19 in Chest X-ray (CXR) images. Several ViT models were fine-tuned for the multiclass classification problem (COVID-19, Pneumonia and Normal cases). A dataset consisting of 7598 COVID-19 CXR images, 8552 CXR for healthy patients and 5674 for Pneumonia CXR were used. The obtained results achieved high performance with an Area Under Curve (AUC) of 0.99 for multi-class classification (COVID-19 vs. Other Pneumonia vs. normal). The sensitivity of the COVID-19 class achieved 0.99. We demonstrated that the obtained results outperformed comparable state-of-the-art models for detecting COVID-19 on CXR images using CNN architectures. The attention map for the proposed model showed that our model is able to efficiently identify the signs of COVID-19. [ABSTRACT FROM AUTHOR]

Due to false negative results of the real-time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test, the complemental practices such as computed tomography (CT) and X-ray in combination with RT-PCR are discussed to achieve a more accurate diagnosis of COVID-19 in clinical practice. Since radiology includes visual understanding as well as decision making under limited conditions such as uncertainty, urgency, patient burden, and hospital facilities, mistakes are inevitable. Therefore, there is an immediate requirement to carry out further investigation and develop new accurate detection and identification methods to provide automatically quantitative evaluation of COVID-19. In this paper, we propose a new computer-aided diagnosis application for COVID-19 detection using deep learning techniques. A new technique, which receives symmetric X-ray data as the input, is presented in this study by combining Convolutional Neural Networks (CNN) with Ant Lion Optimization Algorithm (ALO) and Multiclass Naïve Bayes Classifier (NB). Moreover, several other classifiers such as Softmax, Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are combined with CNN. The promising results of these classifiers are evaluated and presented for accuracy, precision, and F1-score metrics. NB classifier with Ant Lion Optimization Algorithm and CNN produced the best results with 98.31% accuracy, 100% precision and 98.25% F1-score and with the lowest execution time. [ABSTRACT FROM AUTHOR]

• We analyze testing protocols used for Covid-19 detection used in the literature. • We successfully classify COVID images whose lungs areas are turned to black. • We try using different datasets for training and testing. • We show that many highly cited paper on this topic use biased testing protocols. In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. We show that similar results can be obtained using X-Ray images that do not contain most of the lungs. We are able to remove the lungs from the images by turning to black the center of the X-Ray scan and training our classifiers only on the outer part of the images. Hence, we deduce that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the dataset that are not correlated to the presence of COVID-19. Finally, we show that creating a fair testing protocol is a challenging task, and we provide a method to measure how fair a specific testing protocol is. In the future research we suggest to check the fairness of a testing protocol using our tools and we encourage researchers to look for better techniques than the ones that we propose. [ABSTRACT FROM AUTHOR]

Diagnosis of thorax diseases is most commonly done by examining the Chest X-rays (CXR). Computer-aided diagnosis (CAD) System assist radiologists in the interpretation of medical images. CAD improves the quality of diagnosis and leverages the productivity of radiologists. Several Deep Learning algorithms have been successfully implemented in order to provide fully-automated, high precision Computer-aided diagnosis (CAD) Systems. In this paper, a dedicated X-ray network trained from scratch for Chest X-Ray image classification is explored. Further, transfer learning using powerful network architecture like MobileNet, InceptionV3, VGG19 and ResNet-50 are investigated in detail. Limited availability of annotated Chest X-ray images makes medical image classification challenging. Transfer learning approach with and without fine-tuning can help overcome this issue by transferring the knowledge gained from pre-trained networks to domain specific tasks. The experimental study is tested on the dataset of Chest X-Ray images, consisting of 2 categories, Pneumonia and Normal. [ABSTRACT FROM AUTHOR]

Threat detection in X-ray security images is critical for preserving public safety. Recently, deep learning algorithms have begun to be adopted for threat detection tasks in X-ray security images. However, most of the prior works in this field have largely focused on using image-level classification and object-level detection approaches. Adopting object separation as a pixel-level approach to analyze X-ray security images can significantly improve automatic threat detection. In this paper, we investigated the effects of incorporating segmentation deep learning models in the threat detection pipeline of a large-scale imbalanced X-ray dataset. We trained a Faster R-CNN (region-based convolutional neural network) model to localize possible threat regions in the X-ray security images on a balanced dataset to maximize detection of true positives. Then, we trained a DeepLabV3+ model to verify the preliminary detections by classifying each pixel in the threat regions, which resulted in the suppression of false positives. The two models were combined in one detection pipeline to produce the final detections. Experiment results demonstrate that the proposed method significantly outperformed previous baseline methods and end-to-end instance segmentation methods, achieving mean average precisions (mAPs) of 94.88%, 91.40%, and 89.42% across increasing scales of imbalance in the practical dataset. [ABSTRACT FROM AUTHOR]

For the purpose of ensuring public security, automatic inspection of X-ray scanners has been deployed at the entry points of many public places to detect dangerous objects. However, current surveillance systems cannot function without human supervision and intervention. In this article, we propose an effective method using deep convolutional neural networks to detect objects during X-ray baggage inspection. As a first step, a large amount of training data is generated by a specific data augmentation technique. Second, a feature enhancement module is used to improve feature extraction capabilities. Then, in order to address the foreground–background imbalance in the region proposal network, focal loss is adopted. Third, the multiscale fused region of interest is utilized to obtain more robust proposals. Finally, soft nonmaximum suppression is adopted to alleviate overlaps in baggage detection. As compared with existing algorithms, the proposed method proves that it is more accurate and robust when dealing with densely cluttered backgrounds during X-ray baggage inspection. [ABSTRACT FROM AUTHOR]

Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure. Pneumonia causes pleural effusion, a condition in which fluids fill the lung, causing respiratory difficulty. Early diagnosis of pneumonia is crucial to ensure curative treatment and increase survival rates. Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We employed deep transfer learning to handle the scarcity of available data and designed an ensemble of three convolutional neural network models: GoogLeNet, ResNet-18, and DenseNet-121. A weighted average ensemble technique was adopted, wherein the weights assigned to the base learners were determined using a novel approach. The scores of four standard evaluation metrics, precision, recall, f1-score, and the area under the curve, are fused to form the weight vector, which in studies in the literature was frequently set experimentally, a method that is prone to error. The proposed approach was evaluated on two publicly available pneumonia X-ray datasets, provided by Kermany et al. and the Radiological Society of North America (RSNA), respectively, using a five-fold cross-validation scheme. The proposed method achieved accuracy rates of 98.81% and 86.85% and sensitivity rates of 98.80% and 87.02% on the Kermany and RSNA datasets, respectively. The results were superior to those of state-of-the-art methods and our method performed better than the widely used ensemble techniques. Statistical analyses on the datasets using McNemar's and ANOVA tests showed the robustness of the approach. The codes for the proposed work are available at https://github.com/Rohit-Kundu/Ensemble-Pneumonia-Detection. [ABSTRACT FROM AUTHOR]

Pneumonia is the leading cause of death worldwide for children under 5 years of age. For pneumonia diagnosis, chest X-rays are examined by trained radiologists. However, this process is tedious and time-consuming. Biomedical image diagnosis techniques show great potential in medical image examination. A model for the identification of pneumonia, trained on chest X-ray images, has been proposed in this paper. The compound scaled ResNet50, which is the upscaled version of ResNet50, has been used in this paper. ResNet50 is a multilayer layer convolution neural network having residual blocks. As it was very difficult to obtain a sufficiently large dataset for detection tasks, data augmentation techniques were used to increase the training dataset. Transfer learning is also used while training the models. The proposed model could help in detecting the disease and can assist the radiologists in their clinical decision-making process. The model was evaluated and statistically validated to overfitting and generalization errors. Different scores, such as testing accuracy, F1, recall, precision and AUC score, were computed to check the efficacy of the proposed model. The proposed model attained a test accuracy of 98.14% and an AUC score of 99.71 on the test data from the Guangzhou Women and Children's Medical Center pneumonia dataset. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts' image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X-ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set. [ABSTRACT FROM AUTHOR]

Pneumonia is a form of acute respiratory infection commonly caused by germs, viruses, and fungi, and can prove fatal at any age. Chest X-rays is the most common technique for diagnosing pneumonia. There have been several attempts to apply transfer learning based on a Convolutional Neural Network to build a stable model in computer-aided diagnosis. Recently, with the appearance of an attention mechanism that automatically focuses on the critical part of the image that is crucial for the diagnosis of disease, it is possible to increase the performance of previous models. The goal of this study is to improve the accuracy of a computer-aided diagnostic approach that medical professionals can easily use as an auxiliary tool. In this paper, we proposed the attention-based transfer learning framework for efficient pneumonia detection in chest X-ray images. We collected features from three-types of pre-trained models, ResNet152, DenseNet121, ResNet18 as a role of feature extractor. We redefined the classifier for a new task and applied the attention mechanism as a feature selector. As a result, the proposed approach achieved accuracy, F-score, Area Under the Curve(AUC), precision and recall of 96.63%, 0.973, 96.03%, 96.23% and 98.46%, respectively. [ABSTRACT FROM AUTHOR]

Due to their open architecture, Internet of Things (IoT) devices have limited user access and are susceptible to various security vulnerabilities. When healthcare data is stored in the cloud, it often becomes vulnerable to side-channel attacks, Denial of Service (DoS) attacks, and eavesdropping. Consequently, there is a need for an efficient access control scheme to enhance the data-sharing processes of IoT devices. Different machine learning and rule-based decision support systems have been proposed to improve the security of cloud-based data storage. However, these models have exhibited increased false alarm rates, complex processing, and longer response times. To address this research gap, we propose a Fuzzy TOPSIS model based on Golden Search Optimization (GSO) to enhance the security of patient information at both IoT and cloud server levels. Additionally, we introduce a revolutionary Diagonal Kernel Convolution Neural Network with an adaptable kernel (DKCNN-AK) to enhance the accuracy of lung disease diagnosis. The GSO algorithm categorizes the security ranks determined by the fuzzy TOPSIS model into the best and worst security principles. We conducted simulations using the COVID-19 X-Ray detection dataset, the lung cancer dataset, and the data collected from IoT devices for analysis.The experiments were conducted to assess various aspects, including computational time, security level analysis, information leakage, privacy level, prediction accuracy, sensitivity, specificity, and confusion matrix. The improved prediction accuracy and security provided by our proposed approach demonstrate its efficiency • The IoT is now playing a vital role in the medical field's approach to providing people. • This paper presents DKCNN-AK architecture for disease prediction. • The DKCNN-AK architecture is used for both disease prediction and severity estimation. • The GSO optimized Fuzzy TOPSIS algorithm is used. [ABSTRACT FROM AUTHOR]

We present an X-ray material classifier region-based convolutional neural network (XMC R-CNN) model for detecting the typical guns and the typical knives in X-ray baggage images. The XMC R-CNN model is used to solve the problem of contraband detection in overlapped X-ray baggage images by the X-ray material classifier algorithm and the organic stripping and inorganic stripping algorithm, and better detection rate and the miss rate are achieved. The detection rates of guns and knives are 96.5% and 95.8%, and the miss rates of guns and knives are 2.2% and 4.2%. The contraband detection technology based on the XMC R-CNN model is applied to X-ray baggage images of security inspection. According to user needs, the safe X-ray baggage images can be automatically filtered in some specific fields, which reduces the number of X-ray baggage images that security inspectors need to screen. The efficiency of security inspection is improved, and the labor intensity of security inspection is reduced. In addition, the security inspector can screen X-ray baggage images according to the boxes of automatic detection, which can improve the effect of security inspection. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) trained on natural images are extremely successful in image classification and localization due to superior automated feature extraction capability. In extending their use to biomedical recognition tasks, it is important to note that visual features of medical images tend to be uniquely different than natural images. There are advantages offered through training these networks on large scale medical common modality image collections pertaining to the recognition task. Further, improved generalization in transferring knowledge across similar tasks is possible when the models are trained to learn modality-specific features and then suitably repurposed for the target task. In this study, we propose modality-specific en- semble learning toward improving abnormality detection in chest X-rays (CXRs). CNN models are trained on a large-scale CXR collection to learn modality-specific features and then repurposed for detecting and localizing abnormalities. Model predictions are combined using different ensemble strategies toward reducing prediction variance and sensitivity to the training data while improving overall performance and generalization. Class-selective relevance mapping (CRM) is used to visualize the learned behavior of the individual models and their ensembles. It localizes discriminative regions of interest (ROIs) showing abnormal regions and offers an improved explanation of model predictions. It was observed that the model ensembles demonstrate superior localization performance in terms of Intersection of Union (IoU) and mean Average Precision (mAP) metrics than any individual constituent model. [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]

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]

COVID-19 quickly became a global pandemic after only four months of its first detection. It is crucial to detect this disease as soon as possible to decrease its spread. The use of chest X-ray (CXR) images became an effective screening strategy, complementary to the reverse transcription-polymerase chain reaction (RT-PCR). Convolutional neural networks (CNNs) are often used for automatic image classification and they can be very useful in CXR diagnostics. In this paper, 21 different CNN architectures are tested and compared in the task of identifying COVID-19 in CXR images. They were applied to the COVIDx8B dataset, a large COVID-19 dataset with 16,352 CXR images coming from patients of at least 51 countries. Ensembles of CNNs were also employed and they showed better efficacy than individual instances. The best individual CNN instance results were achieved by DenseNet169, with an accuracy of 98.15% and an F1 score of 98.12%. These were further increased to 99.25% and 99.24%, respectively, through an ensemble with five instances of DenseNet169. These results are higher than those obtained in recent works using the same dataset. • This paper compares 21 CNN architectures for COVID-19 detection in CXR images. • DenseNet169 achieved an accuracy of 98.15% and an F1 score of 98.12%. • An ensemble of DenseNet169 instances increased the F1 score to 99.24%. • The training was repeated five times for each model to get more reliable results. [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]

Despite advances in Deep Learning, the Convolutional Neural Networks methods still manifest limitations in medical applications because datasets are usually restricted in the number of samples or include poorly contrasted images. Such a case is found in stenosis detection using X-rays coronary angiography. In this study, the emerging field of quantum computing is applied in the context of hybrid neural networks. So, a hybrid transfer-learning paradigm is used for stenosis detection, where a quantum network drives and improves the performance of a pre-trained classical network. An intermediate layer between the classical and quantum network post-processes the classical features by mapping them into a hypersphere of fixed radius through a hyperbolic tangent function. Next, these normalized features are processed in the quantum network, and through a SoftMax function, the class probabilities are obtained: stenosis and non-stenosis. Furthermore, a distributed variational quantum circuit is implemented to split the data into multiple quantum circuits within the quantum network, improving the training time without compromising the stenosis detection performance. The proposed method is evaluated on a small X-ray coronary angiography dataset containing 250 image patches (50%–50% of positive and negative stenosis cases). The hybrid classical–quantum network significantly outperformed the classical network. Evaluation results showed a boost concerning the classical transfer learning paradigm in the accuracy of 9%, recall of 20%, and F 1 -score of 11%, reaching 91.8033%, 94.9153%, and 91.8033%, respectively. • A quantum network boosts the performance of classical neural architecture. • An L 2 hyperbolic tangent layer bounds the features between classical and quantum stages. • The X-ray angiography images are analyzed to develop a robust stenosis detector system. • Transfer learning and quantum network substantially improved stenosis detection. • An efficient hybrid classical–quantum architecture is focused on stenosis detection. [ABSTRACT FROM AUTHOR]

COVID-19 is a respiratory illness that has affected a large population worldwide and continues to have devastating consequences. It is imperative to detect COVID-19 at the earliest opportunity to limit the span of infection. In this work, we developed a new CNN architecture STM-RENet to interpret the radiographic patterns from X-ray images. The proposed STM-RENet is a block-based CNN that employs the idea of split–transform–merge in a new way. In this regard, we have proposed a new convolutional block STM that implements the region and edge-based operations separately, as well as jointly. The systematic use of region and edge implementations in combination with convolutional operations helps in exploring region homogeneity, intensity inhomogeneity, and boundary-defining features. The learning capacity of STM-RENet is further enhanced by developing a new CB-STM-RENet that exploits channel boosting and learns textural variations to effectively screen the X-ray images of COVID-19 infection. The idea of channel boosting is exploited by generating auxiliary channels from the two additional CNNs using Transfer Learning, which are then concatenated to the original channels of the proposed STM-RENet. A significant performance improvement is shown by the proposed CB-STM-RENet in comparison to the standard CNNs on three datasets, especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), accuracy (96.53%), and reasonable F-score (95%) of the proposed technique suggest that it can be adapted to detect COVID-19 infected patients. [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]

X-ray flaw detection is a key link in the detection of internal defects in titanium alloy castings which are used for most important components in aeroengines. However, the existing manual defect detection methods from the X-ray images have common drawbacks such as unstable artificial recognition, misdetection, misjudgment, fails of quantitative analysis, huge workload, and low-quality inspection efficiency. To avoid these drawbacks, this paper proposes a new artificial intelligent (AI) method to detect and recognize the aerospace titanium casting defects from the X-ray images. It includes the target defect positioning method named as filtered selective search algorithm (FSS) and the defect classification method named as evenly distributed convolutional neural network (ED-CNN). In the target positioning step, through statistical analysis of defect characteristics, a filtered selective search algorithm is built with two filters (size and edge curvature). In this way, the FSS algorithm can position the defects with almost 100 % of accuracy, hence avoid missed detection and false detection. In the target classification step, an ED-CNN is constructed with a similar structure of the same number of layers in each feature extraction stage, and its entire architecture is evenly distributed. Compared with other three classic high-performance convolutional neural network models (AlexNet, VGG16 and VGG19), the ED-CNN model has the best performance. The ED-CNN model was tested with 324 targets from 50 original images, a classification accuracy of nearly 90 % was obtained for low density holes, porosity, linear defects, high density inclusions and casting structure. The FSS/ED-CNN method of two phases defect detection proposed in this paper can achieve accurate positioning and high accurate classification of typical defect targets, and is expected to solve the common drawbacks of "manual defect detection". The newly-proposed FSS/ED-CNN method has important research significance and engineering value. [ABSTRACT FROM AUTHOR]

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11 % , thus being particularly suitable for the early detection of COVID-19. [ABSTRACT FROM AUTHOR]

Coronary artery disease is the most frequent type of heart disease caused by an abnormal narrowing of coronary arteries, also called stenosis or atherosclerosis. It is also the leading cause of death globally. Currently, X-ray Coronary Angiography (XCA) remains the gold-standard imaging technique for medical diagnosis of stenosis and other related conditions. This paper presents a new method for the automatic detection of coronary artery stenosis in XCA images, employing a pre-trained (VGG16, ResNet50, and Inception-v3) Convolutional Neural Network (CNN) via Transfer Learning. The method is based on a network-cut and fine-tuning approach. The optimal cut and fine-tuned layers were selected following 20 different configurations for each network. The three networks were fine-tuned using three strategies: only real data, only artificial data, and artificial with real data. The synthetic dataset consists of 10,000 images ( 80 % for training, 20 % for validation) produced by a generative model. These different configurations were analyzed and compared using a real dataset of 250 real XCA images (125 for testing and 125 for fine-tuning), regarding their randomly initiated CNNs and a fourth custom CNN, trained as well with artificial and real data. The results showed that pre-trained VGG16, ResNet50, and Inception-v3 cut on an early layer and fine-tuned, overcame the referencing CNNs performance. Specifically, Inception-v3 provided the best stenosis detection with an accuracy of 0.95 , a precision of 0.93 , sensitivity, specificity, and F 1 score of 0.98 , 0.92 , and 0.95 , respectively. Moreover, a class activation map is applied to identify the high attention regions for stenosis detection. [ABSTRACT FROM AUTHOR]