Deep Learning Approach for Diagnosis of Thoracic Diseases from Chest X-Ray Images

2021 ◽  
Author(s):  
Siddhika Arunachalam ◽  
Prasenjit Bhavathankar
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
X Ray ◽  
Thoracic Diseases ◽  
Chest X Ray

Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures

Measurement ◽  
2021 ◽  
pp. 109953
Author(s):  
Adhiyaman Manickam ◽  
Jianmin Jiang ◽  
Yu Zhou ◽  
Abhinav Sagar ◽  
Rajkumar Soundrapandiyan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Approach ◽  
X Ray ◽  
Chest X Ray ◽  
Learning Architectures

Early Detection of Pneumonia from Chest X-Ray Images Using Deep Learning Approach

2021 ◽  
pp. 595-604
Author(s):  
Prateek Sarangi ◽  
Pradosh Priyadarshan ◽  
Swagatika Mishra ◽  
Adyasha Rath ◽  
Ganapati Panda
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Learning Approach ◽  
X Ray ◽  
Chest X Ray

scholarly journals Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images

2020 ◽  
Author(s):  
Muntasir Al-Asfoor
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Normal Subjects ◽  
Learning Approach ◽  
Computer Aided Detection ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Chest X Ray ◽  
The Times

Abstract During the times of pandemics, faster diagnosis plays a key role in the response efforts to contain the disease as well as reducing its spread. Computer-aided detection would save time and increase the quality of diagnosis in comparison with manual human diagnosis. Artificial Intelligence (AI) through deep learning is considered as a reliable method to design such systems. In this research paper, an AI based diagnosis approach has been suggested to tackle the COVID-19 pandemic. The proposed system employs a deep learning algorithm on chest x-ray images to detect the infected subjects. An enhanced Convolutional Neural Network (CNN) architecture has been designed with 22 layers which is then trained over a chest x-ray dataset. More after, a classification component has been introduced to classify the x-ray images into two categories (Covid-19 and not Covid-19) of infection. The system has been evaluated through a series of observations and experimentation. The experimental results have shown a promising performance in terms of accuracy. The system has diagnosed Covid-19 with accuracy of 95.7% and normal subjects with accuracy of 93.1 while it showed 96.7 accuracy on Pneumonia.

scholarly journals Multi-diseases Classification from Chest-X-ray: A Federated Deep Learning Approach

2020 ◽  
pp. 3-15
Author(s):  
Sourasekhar Banerjee ◽  
Rajiv Misra ◽  
Mukesh Prasad ◽  
Erik Elmroth ◽  
Monowar H. Bhuyan
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
X Ray ◽  
Chest X Ray

scholarly journals Deteksi Penyakit Covid-19 Berdasarkan Citra X-Ray Menggunakan Deep Residual Network

2020 ◽  
Vol 8 (2) ◽  
pp. 443
Author(s):  
YULI SUN HARIYANI ◽  
SUGONDO HADIYOSO ◽  
THOMHERT SUPRAPTO SIADARI
Keyword(s):  
Deep Learning ◽  
Clinical Diagnosis ◽  
Learning Approach ◽  
Residual Network ◽  
X Ray ◽  
Global Pandemic ◽  
X Ray Imaging ◽  
Clinical Diagnoses ◽  
Chest X Ray ◽  
Supporting Tool

ABSTRAKPenyakit Coronavirus-2019 atau Covid-19 telah menjadi pandemi global dan menjadi masalah utama yang harus segera dikendalikan. Salah satu cara yang dapat dilakukan adalah memutus rantai penyebaran virus tersebut dengan melakukan deteksi dan melalukan karantina. Pencitraan X-Ray dapat dijadikan alternatif dalam mempelajari Covid-19. X-Ray dianggap mampu menggambarkan kondisi paru-paru pada pasien Covid-19 dan dapat menjadi alat bantu diagnosa klinis. Pada penelitian ini, kami mengusulkan pendekatan deep learning berbasis residual deep network untuk deteksi Covid-19 melalui citra chest X-Ray. Evaluasi yang dilakukan untuk mengetahui performa metode yang diusulkan berupa precision, recall, F1, dan accuracy. Hasil eksperimen menunjukkan bahwa usulan metode ini memberikan precision, recall, F1 dan accuracy masing-masing 0,98, 0,95, 0,97 dan 99%. Pada masa mendatang, studi ini diharapkan dapat divalidasi dan kemudian digunakan untuk melengkapi diagnosa klinis oleh dokter.Kata kunci: Coronavirus-2019, Covid-19, chest X-Ray, deep learning, residual network ABSTRACTCoronavirus-2019 or Covid-19 disease has become a global pandemic and is a major problem that must be stopped immediately. One of the ways that can be done to stop its spreading is to break the spreading chain of the virus by detecting and doing quarantine. X-Ray imaging can be used as an alternative in detecting Covid-19. X-Ray is considered able to describe the condition of the lungs for Covid-19 suspected patients and can be a supporting tool for clinical diagnosis. In this study, we propose a residual based deep learning approach for Covid-19 detection using chest X-Ray images. Evaluation is carried out to determine the performance of the proposed method in the form of precision, recall, F1 and accuracy. Experiments results show that our proposed method provides precision, recall, F1 and accuracy respectively 0.98, 0.95, 0.97 and 99%. In the future, this study is expected to be validated and then used to support clinical diagnoses by doctors.Keywords: Coronavirus-2019, Covid-19, chest X-Ray, deep learning, residual network

scholarly journals Pneumonia Detection from Chest X-Rays using Neural Networks

2021 ◽  
Vol 9 (VII) ◽  
pp. 910-913
Author(s):  
Ashish Jain
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Great Success ◽  
X Rays ◽  
X Ray ◽  
Open Set ◽  
The World ◽  
Chest X Ray ◽  
Bacteria And Fungi ◽  
Inflammation Of The Lungs

Pneumonia is one of the most serious diseases which cause the most deaths in the world. Viruses, bacteria, and fungi can cause pneumonia. The infection from spreading to the lungs in the human body. In order to diagnose this infection, a chest x-ray is carried out. The doctor uses X-ray image in order to diagnose or monitor the treatment of states in which inflammation of the lungs. X-rays are also used in the diagnosis of diseases such as emphysema, lung cancer, cancer of the line, and pipe, and tuberculosis (tb). However, a diagnosis of pneumonia requiring medical experts to comment on its presence felt in the chest x-ray. For decades, the auto- diagnosis (CAD) systems have been used for the respiratory disease based on chest X-ray images. Deep learning allows machines can quickly extract and classify objects from a photo. Ilham, with the great success of deep learning, we use a deep learning approach to detection of pneumonia into the work. Convolutional neural network that was developed for this study is the inflammation of the lungs. Supervised learning is ANCHORED to the use of features and functions. In general, the data of 5826 images with the help of one of the Kaggle.com. The CNN training and testing, that is, an open set of data. In the proposed method, the high success rate of accurate classification is achieved.

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