Lung Lobe Segmentation and Feature Extraction-Based Hierarchical Attention Network for COVID-19 Prediction from Chest X-Ray Images

Coronavirus disease 2019 (COVID-19) is a rising respiratory sickness. It causes harsh pneumonia and is considered to cover higher collisions in the healthcare domain. The diagnosis at an early stage is more complex to get accurate treatment for reducing the stress in the clinical sector. Chest X-ray scan is the standard imaging diagnosis test employed for pneumonia disease. Automatic detection of COVID-19 helps to control the community outbreak but tracing this viral infection through X-ray results in a challenging task in the medical community. To automatically detect the viral disease in order to reduce the mortality rate, an effective COVID-19 detection method is modelled in this research by the proposed manta-ray multi-verse optimization-based hierarchical attention network (MRMVO-based HAN) classifier. Accordingly, the MRMVO is the incorporation of manta-ray foraging optimization and multi-verse optimizer. Based on the segmented lung lobes, the features are acquired from segmented regions in such a way that the process of COVID-19 detection mechanism is carried out with the features acquired from interested lobe regions. The proposed method has good performance with the measures, such as accuracy, true positive rate and true negative rate with the values of 93.367, 89.921 and 95.071%. [ABSTRACT FROM AUTHOR] Copyright of Computer Journal is the property of Oxford University Press / USA 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Capsule Networks with Chest X-Ray Enhancement for Detection of COVID-19

Corona virus was declared a global pandemic that has affected people worldwide. It is critical to diagnose corona virus-infected individuals to restrict the virus's transmission. Recent research indicates that radiological methods provide valuable information in identifying infection using deep learning algorithms. Deep learning has contributed to large-scale medical data research, providing new ways and chances for diagnostic tools. This research attempted to investigate how the Capsule Networks leverage chest X-ray scans to identify the infected person. We suggest Capsule Networks identify the illness using chest X-ray data. The proposed approach is rapid and robust, classifying scans into COVID-19, No Findings, or any other issue in the lungs. The study can be used as a preliminary diagnosis by medical practitioners, and the study focuses on the COVID-19 class, a minority class in all public data sets accessible, and ensures that no COVID-19 infected individual is identified as Normal. Even with a small dataset, the model provides 96.37% accuracy for COVID-19 and for the non-COVID-19, and on multi-class classification, it provides an accuracy of 95.12%. © 2022 IEEE.

Identification of Coronavirus & Pneumonia from Chest x-ray Scans using Deep Multilayered CNN

Recently, COVID-19 disease carried out by the SARS-CoV-2 virus appeared as a pandemic across the world. The traditional diagnostic techniques are facing a hard time detecting the virus efficiently at an early stage. In this context, chest x-ray scans can be useful for diagnostic prediction. Therefore, in this paper, a deep multi-layered convolution neural network has been proposed to analyze the chest x-ray scans effectively for detecting COVID-19 and pneumonia accurately. The proposed approach has been applied on multiple benchmark datasets and the experimental results define the effectiveness of the proposed approach. © 2021 IEEE.

Adaptive Self-Paced Transfer Learning for COVID-19 Diagnosis with CXR Images

Coronavirus Disease 2019 (COVID-19) has become an unprecedented public health crisis since December of 2019. Compared with real-time reverse transcription polymerase chain reaction (rRT-PCR), the computer-aided diagnosis machine learning algorithm based on medical images can vastly ease the burden on clinicians. Even so, despite existing hundreds of millions of confirmed cases worldwide, there has not been a mature, large scale, high quality, single standard shared image data set yet, which can lead to some problems. For instance, 1) Because the sources of medical images and the collection standards are not guaranteed, features extracted by the neural network may not be very ideal. 2) Due to the small number of samples, some outliers (e.g., blurry medical images, inconspicuous symptoms) may significantly descend the performance of the model. To address these problems, we propose an adaptive self-paced transfer learning (ASPTL) algorithm in this paper. Specifically, inspired by the process of human learning from easy to difficult, we also evaluated the learning difficulty of the samples. Samples with no obvious disease features or wrong labels are relatively difficult to diagnose, and the samples that are easy to diagnose are selected adaptively in the iterative process. In addition, we adopt transfer learning to select easy to learn samples on the pre-trained network by self-paced learning, and gradually fine-tune the pre-trained model in an iterative way. We designed two experiments to validate the ASPTL algorithm's performance on COVID-19. The reult prove the effectiveness on solving mentioned problems. © 2021 IEEE.

Diagnosis of COVID-19 and Pneumonia using Depthwise Separable Convolutional Neural Network

COVID-19 disease is a consequence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that came to light as an epidemic over the planet. The long-established diagnostic systems are confronting difficulties in identifying the virus expeditiously in the initial stages. In these circumstances, chest X-ray scans can be beneficial for the identification of COVID-19 as well as pneumonia. On that account, in this research, a deep convolution neural network having depthwise separable convolutions has been put forward to look over the chest X-ray scans for identifying COVID-19 and pneumonia precisely. The propounded model with only 0.18 million parameters has been employed on various standard datasets and performs significantly faster than other state-of-the-art models and the exploratory results explain the potency of the propounded approach. © 2021 IEEE.