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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]

• 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]

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]

• 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]

• 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]