A novel framework based on deep learning for COVID-19 diagnosis from X-ray images.

Background: The coronavirus infection has endangered human health because of the high speed of the outbreak. A rapid and accurate diagnosis of the infection is essential to avoid further spread. Due to the cost of diagnostic kits and the availability of radiology equipment in most parts of the world, the COVID-19 detection method using X-ray images is still used in underprivileged countries. However, they are challenging due to being prone to human error, time-consuming, and demanding. The success of deep learning (DL) in automatic COVID-19 diagnosis systems has necessitated a detection system using these techniques. The most critical challenge in using deep learning techniques in diagnosing COVID-19 is accuracy because it plays an essential role in controlling the spread of the disease. Methods: This article presents a new framework for detecting COVID-19 using X-ray images. The model uses a modified version of DenseNet-121 for the network layer, an image data loader to separate images in batches, a loss function to reduce the prediction error, and a weighted random sampler to balance the training phase. Finally, an optimizer changes the attributes of the neural networks. Results: Extensive experiments using different types of pneumonia expresses satisfactory diagnosis performance with an accuracy of 99.81%. Conclusion: This work aims to design a new deep neural network for highly accurate online recognition of medical images. The evaluation results show that the proposed framework can be considered an auxiliary device to help radiologists accurately confirm initial screening.

Weighted semantic plagiarism detection approach based on AHP decision model.

The increasing rate of academic plagiarism is a social problem that engages institutions and publishers. Plagiarists try to mislead the plagiarism detection system using synonyms and inverted word order. Numerous algorithms tried to overcome these problems using structural and semantic detection. However, most of them focus on overcoming some challenges. Moreover, all of them consider the same significant degree for all terms of the documents. On the other hand, the time complexity is an essential parameter that must be considered. This paper presents an effective way to detect structural and semantic similarity degrees among two papers only using some part of the paper's content instead of all content, decreasing the time complexity. The similarity is calculated using a set of impressive terms and various combinations to augment plagiarism detection ability even if the word order is changed. Different weight is assigned to the word according to its position in various sections of the paper. Finally, an AHP (Analytical Hierarchy Process) model uses to calculate a weighted similarity. The results indicated that the proposed approach has more ability to detect semantic academic plagiarism, and the runtime is reduced compared to similar ones.

A novel deep learning based method for COVID-19 detection from CT image.

The novel Coronavirus named COVID-19 that World Health Organization (WHO) announced as a pandemic rapidly spread worldwide. Fast diagnosis of the virus infection is critical to prevent further spread of the virus, help identify the infected population, and cure the patients. Due to the increasing rate of infection and the limitations of the diagnosis kit, auxiliary detection tools are needed. Recent studies show that a deep learning model that comes up with the salient information of CT images can aid in the COVID-19 diagnosis. This study proposes a novel deep learning structure that the pooling layer of this model is a combination of pooling and the Squeeze Excitation Block (SE-block) layer. The proposed model uses Batch Normalization and Mish Function to optimize convergence time and performance of COVID-19 diagnosis. A dataset of two public hospitals was used to evaluate the proposed model. Moreover, it was compared to some different popular deep neural networks (DNN). The results expressed an accuracy of 99.03 with a recognition time of test mode of 0.069 ms in graphics processing unit (GPU). Furthermore, the best network results in classification metrics parameters and real-time applications belong to the proposed model.