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Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model – EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model’s efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Timely discovery of COVID-19 could aid in formulating a suitable treatment plan for disease mitigation and containment decisions. The widely used COVID-19 test necessitates a regular method and has a low sensitivity value. Computed tomography and chest X-ray are also other methods utilized by numerous studies for detecting COVID-19. In this article, we propose a CNN called depthwise separable convolution network with wavelet multiresolution analysis module (WMR-DepthwiseNet) that is robust to automatically learn details from both spatialwise and channelwise for COVID-19 identification with a limited radiograph dataset, which is critical due to the rapid growth of COVID-19. This model utilizes an effective strategy to prevent loss of spatial details, which is a prevalent issue in traditional convolutional neural network, and second, the depthwise separable connectivity framework ensures reusability of feature maps by directly connecting previous layer to all subsequent layers for extracting feature representations from few datasets. We evaluate the proposed model by utilizing a public domain dataset of COVID-19 confirmed case and other pneumonia illness. The proposed method achieves 98.63% accuracy, 98.46% sensitivity, 97.99% specificity, and 98.69% precision on chest X-ray dataset, whereas using the computed tomography dataset, the model achieves 96.83% accuracy, 97.78% sensitivity, 96.22% specificity, and 97.02% precision. According to the results of our experiments, our model achieves up-to-date accuracy with only a few training cases available, which is useful for COVID-19 screening. This latest paradigm is expected to contribute significantly in the battle against COVID-19 and other life-threatening diseases. [ABSTRACT FROM AUTHOR]

SARS-CoV-2, a member of the coronavirus family, causes COVID-19, which can range from the ordinary cold to the rare but fatal Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Disease (MERS). Radiologists can better diagnose COVID-19 using deep learning and computer vision techniques. However, training requires large centralized datasets. In addition, medical data protection prevents centralized server training with medical data. In this study, we propose a federated learning-based framework, Cov-Fed, to facilitate decentralized training of medical data. Cov-Fed is characterized by the proposed Multi-Efficient Channel Attention Network (MECAN) and a multi-model user-server weight update mechanism, respectively. In addition, Cov-Fed leverages PySyft encryption and decryption mechanisms for privacy preservation. Due to the salient nature of features corresponding to viruses, the classification of x-ray scans is challenging. Hence, we propose a multi-ECA attention mechanism embedded in the Cov-Fed to enhance feature maps of chest X-ray scans for classification without compromising performance. Extensive experiments are conducted on several public medical datasets and compared with some state-of-the-art models. Based on several evaluation metrics, Cov-Fed exhibited satisfactory performance with an average accuracy of 87. 65 % , an inference time of 92. 8 s , and ROC-AUC of 92. 46 % compared with some state-of-the-art models. [ABSTRACT FROM AUTHOR]