scholarly journals A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset

2021 ◽  
Vol 7 ◽  
pp. e364
Author(s):  
Omar M. Elzeki ◽  
Mohamed Abd Elfattah ◽  
Hanaa Salem ◽  
Aboul Ella Hassanien ◽  
Mahmoud Shams
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Imaging Method ◽  
Fusion Algorithm ◽  
Algorithm Performance ◽  
Patient Status ◽  
Layer Image ◽  
Chest X Ray ◽  
Almost All

Background and Purpose COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people’s health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance. Materials and Methods In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used. Results Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status. Conclusions A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.

Impact Analysis of Machine Learning Techniques for COVID-19 Diagnosis: A Critical Review

2021 ◽  
Vol 12 (2) ◽  
pp. 510-516
Author(s):  
Anshul, Et. al.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Severe Acute Respiratory Syndrome ◽  
Impact Analysis ◽  
Machine Learning Techniques ◽  
X Ray ◽  
Learning Techniques ◽  
The World ◽  
Almost All

COVID-19 virus belongs to the severe acute respiratory syndrome (SARS) family raised a situation of health emergency in almost all the countries of the world. Numerous machine learning and deep learning based techniques are used to diagnose COVID positive patients using different image modalities like CT SCAN, X-RAY, or CBX, etc. This paper provides the works done in COVID-19 diagnosis, the role of ML and DL based methods to solve this problem, and presents limitations with respect to COVID-19 diagnosis.

The Role of Spatial Alignment in Multimodal Medical Image Fusion Using Deep Learning for Diagnostic Problems

2021 ◽  
Author(s):  
Xingyue Wang ◽  
Kuang Shu ◽  
Haowei Kuang ◽  
Shiwei Luo ◽  
Richu Jin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Medical Image ◽  
Medical Image Fusion ◽  
Diagnostic Problems ◽  
Spatial Alignment

Deep learning supported disease detection with multi-modality image fusion

2021 ◽  
pp. 1-24
Author(s):  
F. Sangeetha Francelin Vinnarasi ◽  
Jesline Daniel ◽  
J.T. Anita Rose ◽  
R. Pugalenthi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Fusion ◽  
Clustering Algorithm ◽  
Medical Images ◽  
Disease Diagnosis ◽  
High Energy ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Fuzzy C Means Clustering

Multi-modal image fusion techniques aid the medical experts in better disease diagnosis by providing adequate complementary information from multi-modal medical images. These techniques enhance the effectiveness of medical disorder analysis and classification of results. This study aims at proposing a novel technique using deep learning for the fusion of multi-modal medical images. The modified 2D Adaptive Bilateral Filters (M-2D-ABF) algorithm is used in the image pre-processing for filtering various types of noises. The contrast and brightness are improved by applying the proposed Energy-based CLAHE algorithm in order to preserve the high energy regions of the multimodal images. Images from two different modalities are first registered using mutual information and then registered images are fused to form a single image. In the proposed fusion scheme, images are fused using Siamese Neural Network and Entropy (SNNE)-based image fusion algorithm. Particularly, the medical images are fused by using Siamese convolutional neural network structure and the entropy of the images. Fusion is done on the basis of score of the SoftMax layer and the entropy of the image. The fused image is segmented using Fast Fuzzy C Means Clustering Algorithm (FFCMC) and Otsu Thresholding. Finally, various features are extracted from the segmented regions. Using the extracted features, classification is done using Logistic Regression classifier. Evaluation is performed using publicly available benchmark dataset. Experimental results using various pairs of multi-modal medical images reveal that the proposed multi-modal image fusion and classification techniques compete the existing state-of-the-art techniques reported in the literature.

scholarly journals An Online Automatic Corona Diagnose System Based on Chest X-ray Images

2020 ◽  
Author(s):  
Reza Amini Gougeh
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Disease Patient ◽  
Small Cities ◽  
X Ray ◽  
Online System ◽  
Patient Status ◽  
X Ray Imaging ◽  
Therapeutic Guidelines ◽  
Chest X Ray

Abstract An outbreak of SARS-CoV-2 shocked healthcare systems around the world. It began in December 2019 in Wuhan, China, and spread out in over 120 countries in less than three months. Imaging technologies helped in COVID-19 fast and reliable diagnosis. CT-Scan and X-ray imaging are popular methods. This study is focused on X-ray imaging, concerning limitations in small cities to access CT-Scan and its costs. Using deep learning models helps to diagnose precisely and quickly. We aimed to design an online system based on deep learning, which reports lung engagement with the disease, patient status, and therapeutic guidelines. Our objective was to relieve pressure on radiologists and minimize the interval between imaging and diagnosing. VGG19, VGG16, InceptionV3, and ResNet50 were evaluated to be considered as the main code of the online diagnosing system. VGG16, with 98.92% accuracy, achieved the best score. VGG19 performed quite similarly to VGG16. VGG19, InceptionV3 and ResNet50 obtained 98.90, 71.79 and 28.27 subsequently.

Michael Kelly, Hilary Footitt, Myriam Salama-Carr (eds.): The Palgrave Handbook of Languages and Conflict

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Ľubomír Zvada
Keyword(s):  
International Relations ◽  
Foreign Languages ◽  
Military Operations ◽  
Sources Of Information ◽  
War And Peace ◽  
Interdisciplinary Field ◽  
Speakers Of Other Languages ◽  
War And Conflict ◽  
Almost All

This Handbook maps the contours of an exciting and burgeoning interdisciplinary field concerned with the role of language and languages in situations of conflict. It explores conceptual approaches, sources of information that are available, and the institutions and actors that mediate language encounters. It examines case studies of the role that languages have played in specific conflicts, from colonial times through to the Middle East and Africa today. The contributors provide vibrant evidence to challenge the monolingual assumptions that have affected traditional views of war and conflict. They show that languages are woven into every aspect of the making of war and peace, and demonstrate how language shapes public policy and military strategy, setting frameworks and expectations. The Handbook's 22 chapters powerfully illustrate how the encounter between languages is integral to almost all conflicts, to every phase of military operations and to the lived experiences of those on the ground, who meet, work and fight with speakers of other languages. This comprehensive work will appeal to scholars from across the disciplines of linguistics, translation studies, history, and international relations; and provide fresh insights for a broad range of practitioners interested in understanding the role and implications of foreign languages in war.

ON EVALUATION OF THE ROLE OF THERMAL AND WIND FACTORS FOR A PHYSICAL MODEL OF THE WORLD OCEAN GENERAL CIRCULATION SYSTEM

2019 ◽  
Vol 47 (3) ◽  
pp. 80-91
Author(s):  
V. G. Neiman
Keyword(s):  
Physical Model ◽  
Ocean Circulation ◽  
General Circulation ◽  
World Ocean ◽  
Circulation System ◽  
The Public ◽  
The World ◽  
Almost All ◽  
Conceptual Foundations

The main content of the work consists of certain systematization and addition of longexisting, but eventually deformed and partly lost qualitative ideas about the role of thermal and wind factors that determine the physical mechanism of the World Ocean’s General Circulation System (OGCS). It is noted that the conceptual foundations of the theory of the OGCS in one form or another are contained in the works of many well-known hydrophysicists of the last century, but the aggregate, logically coherent description of the key factors determining the physical model of the OGCS in the public literature is not so easy to find. An attempt is made to clarify and concretize some general ideas about the two key blocks that form the basis of an adequate physical model of the system of oceanic water masses motion in a climatic scale. Attention is drawn to the fact that when analyzing the OGCS it is necessary to take into account not only immediate but also indirect effects of thermal and wind factors on the ocean surface. In conclusion, it is noted that, in the end, by the uneven flow of heat to the surface of the ocean can be explained the nature of both external and almost all internal factors, in one way or another contributing to the excitation of the general, or climatic, ocean circulation.

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