Mixed attention and regularized COVID‐19 network: An approach to detection of COVID‐19 with chest x‐ray images

Coronavirus Disease 2019 (COVID‐19) has led to a global pandemic in the year 2020 and the cases are dynamically increasing and active all over the world. COVID‐19 is caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2). It is a human‐to‐human transmissible disease which has severely affected people especially with weaker immunity, and is detected through Reverse Transcription Polymerase Chain Reaction (RT‐PCR). RT‐PCR is a lethargic process and therefore intelligent systems are proposed which uses chest images for early detection of COVID‐19. This paper proposes a regularized and attentive intelligent system called ‘Mixed Attention & Regularized COVID‐19 Network (MARCOV19‐Net)' for detection of COVID‐19 using chest X‐Ray images. The performance of MARCOV19‐Net is compared with VGG‐16, Regularized COVID‐19 Deep Convolutional Network (RCOV19‐DCNet) and Mixed Attention and unregularized COVID‐19 Network (MACOV19‐Net), and with other state‐of‐the‐art models. MARCOV19‐Net has achieved the highest F‐score, ROC and AUC of 98.76%, 99.4% and 99.6%, respectively. [ FROM AUTHOR] Copyright of International Journal of Imaging Systems & Technology is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A novel centroid based sentence classification approach for extractive summarization of COVID-19 news reports.

A COVID-19 news covers subtopics like infections, deaths, the economy, jobs, and more. The proposed method generates a news summary based on the subtopics of a reader's interest. It extracts a centroid having the lexical pattern of the sentences on those subtopics by the frequently used words in them. The centroid is then used as a query in the vector space model (VSM) for sentence classification and extraction, producing a query focused summarization (QFS) of the documents. Three approaches, TF-IDF, word vector averaging, and auto-encoder are experimented to generate sentence embedding that are used in VSM. These embeddings are ranked depending on their similarities with the query embedding. A Novel approach has been introduced to find the value for the similarity parameter using a supervised technique to classify the sentences. Finally, the performance of the method has been assessed in two different ways. All the sentences of the dataset are considered together in the first assessment and in the second, each document wise group of sentences is considered separately using fivefold cross-validation. The proposed method has achieved a minimum of 0.60 to a maximum of 0.63 mean F1 scores with the three sentence encoding approaches on the test dataset.

Perspective of AI system for COVID-19 detection using chest images: a review

Coronavirus Disease 2019 (COVID-19) is an evolving communicable disease caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which has led to a global pandemic since December 2019. The virus has its origin from bat and is suspected to have transmitted to humans through zoonotic links. The disease shows dynamic symptoms, nature and reaction to the human body thereby challenging the world of medicine. Moreover, it has tremendous resemblance to viral pneumonia or Community Acquired Pneumonia (CAP). Reverse Transcription Polymerase Chain Reaction (RT-PCR) is performed for detection of COVID-19. Nevertheless, RT-PCR is not completely reliable and sometimes unavailable. Therefore, scientists and researchers have suggested analysis and examination of Computing Tomography (CT) scans and Chest X-Ray (CXR) images to identify the features of COVID-19 in patients having clinical manifestation of the disease, using expert systems deploying learning algorithms such as Machine Learning (ML) and Deep Learning (DL). The paper identifies and reviews various chest image features using the aforementioned imaging modalities for reliable and faster detection of COVID-19 than laboratory processes. The paper also reviews and compares the different aspects of ML and DL using chest images, for detection of COVID-19.

Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach.

Coronavirus Disease (COVID19) is a fast-spreading infectious disease that is currently causing a healthcare crisis around the world. Due to the current limitations of the reverse transcription-polymerase chain reaction (RT-PCR) based tests for detecting COVID19, recently radiology imaging based ideas have been proposed by various works. In this work, various Deep CNN based approaches are explored for detecting the presence of COVID19 from chest CT images. A decision fusion based approach is also proposed, which combines predictions from multiple individual models, to produce a final prediction. Experimental results show that the proposed decision fusion based approach is able to achieve above 86% results across all the performance metrics under consideration, with average AUROC and F1-Score being 0.883 and 0.867, respectively. The experimental observations suggest the potential applicability of such Deep CNN based approach in real diagnostic scenarios, which could be of very high utility in terms of achieving fast testing for COVID19.