scholarly journals Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images

2020 ◽  
Vol 129 ◽  
pp. 271-278 ◽  
Author(s):  
Abhir Bhandary ◽  
G. Ananth Prabhu ◽  
V. Rajinikanth ◽  
K. Palani Thanaraj ◽  
Suresh Chandra Satapathy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
Lung Ct ◽  
Lung Ct Scan

scholarly journals Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images

2021 ◽  
Author(s):  
Vinayakumar Ravi ◽  
Harini Narasimhan ◽  
Chinmay Chakraborty ◽  
Tuan D. Pham
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
X Ray ◽  
Chest X Ray

scholarly journals A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images

2021 ◽  
Vol 11 (1) ◽  
pp. 844
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Early Stage ◽  
Diagnostic Technique ◽  
Disease Diagnosis ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
Low Sensitivity ◽  
Deep Learning Model

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.

Explainable Deep Learning for Covid-19 Detection Using Chest X-ray and CT-Scan Images

2021 ◽  
pp. 311-336
Author(s):  
Sidi Ahmed Mahmoudi ◽  
Sédrick Stassin ◽  
Mostafa El Habib Daho ◽  
Xavier Lessage ◽  
Saïd Mahmoudi
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
X Ray ◽  
Chest X Ray

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.

scholarly journals DenseCapsNet: Detection of COVID-19 X-ray Images Using a Capsule Network

2020 ◽  
Author(s):  
Hao Quan ◽  
Xiaosong Xu ◽  
Tingting Zheng ◽  
Zhi Li ◽  
Mingfang Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Augmentation ◽  
Detection Performance ◽  
Convolution Neural Network ◽  
X Ray ◽  
Learning Framework ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Novel Coronavirus

Abstract Objective: A deep learning framework for detecting COVID-19 is developed, and a small amount of chest X-ray data is used to accurately screen COVID-19.Methods: In this paper, we propose a deep learning framework that integrates convolution neural network and capsule network. DenseNet and CapsNet fusion are used to give full play to their respective advantages, reduce the dependence of convolution neural network on a large amount of data, and can quickly and accurately distinguish COVID-19 from Non-COVID-19 through chest X-ray imaging.Results: A total of 1472 chest X-ray COVID-19 and non-COVID-19 images are used, this method can achieve an accuracy of 99.32% and a precision of 100%, with 98.55% sensitivity and 100% specificity.Conclusion: These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia X-ray detection. We also prove through experiments that the detection performance of DenseCapsNet is not affected fundamentally by a lack of data augmentation and pre-training.

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