Parkinson’s disease (PD) is a degenerative and progressive neurological condition . Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTscan . The proposed system may effectively aid medical workers in the early diagnosis of Parkinson’s Disease . It could be concluded that the proposed system, in […]

The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert . We propose a novel embedding generation technique that leverages SciBERT language model pretrained on a large multi-domain corpus of scientific publications and fine-tuned for domain adaptation on the CORD-19 dataset . The conducted manual evaluation by […]

The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia . The current diagnostic procedure of COVID-20 follows reverse-transcriptase polymerase polymerases polymerase chain reaction (RT-PCR) based approach . This article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches . […]

Pneumonia is an infection that inflames the lungs’ air sacs of a human . Using a combination of GAN and deep transfer models proved it is efficiency according to testing accuracy measurement . AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to detect the pneumonia from chest x-rays . The research […]

Standard breast cancer screening involves the acquisition of two mammography X-ray projections for each breast . We introduce a deep learning, patch-based Siamese network for lesion matching in dual-view mammography . Our locally-fitted approach generates a joint patch pair representation and comparison with a shared configuration between the two views . We performed a comprehensive […]

Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients . The reduction of CT radiation dose compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance . A novel feature of our approach is that an initial […]

Study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray images . The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods . The three-category accuracy of the CAD system was 83.6% […]

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Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images… The major challenge lies in the inadequate public public COVI-19 datasets . The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general […]

The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel . X-ray imaging is an easily accessible tool that can be an excellent alternative in the diagnosis . A public database was created by the authors […]

COVID-CXNet uses deep convolutional neural networks in a large dataset . It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making . This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization . It’s […]

Online search query frequency time series to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on identified symptom categories by United Kingdom’s National Health Service. We then propose ways for minimising an expected bias in these signals partially generated by the early and continuous exposure to […]

The pandemic of COVID-19 has caused millions of infectious. Due to the false-negative rate and the time cost of conventional RT-PCR tests, X-ray images and Computed Tomography (CT) images based diagnosing become widely adopted. Researchers of the computer vision area have developed many automatic diagnosing models to help radiologists and pro-mote the diagnosing accuracy. 62 […]

The spread of COVID-19 has become a significant and troubling aspect of society in 2020. Many disease detection models do not incorporate the wealth of social media data that can be utilized for modeling and predicting its spread. To answer this, we propose the task of cross-lingual transfer learning for epidemiological alignment. Utilizing both macro […]

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID 19-related consultations. We collected […]

TX-Ray expresses neurons as feature preference distributions to quantify fine-grained knowledge transfer or adaptation and guide human analysis. It can identify prunable neurons for model compression with improved test set generalization and it can reveal how early stages of self-supervision automatically learn linguistic abstractions like parts-of-speech. We find that TX- Ray can identify Prunable Neurons […]

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