scholarly journals Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images

2021 ◽  
Author(s):  
N. Kumar ◽  
M. Gupta ◽  
D. Gupta ◽  
S. Tiwari
Keyword(s):  
Transfer Learning ◽  
Learning Model ◽  
X Ray

scholarly journals Pneumonia Detection using Artificial Neural Networks and Transfer Learning Model

2019 ◽  
Vol 8 (2S4) ◽  
pp. 149-151
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Transfer Learning ◽  
Learning Model ◽  
High Accuracy ◽  
Lung Region ◽  
X Ray ◽  
Otsu Thresholding ◽  
Chest X Ray ◽  
Healthy Lung

Developing a system that helps in detecting pneumonia in chest x-ray images of lungs at a high accuracy. Firstly, a raw image is being preprocessed with the help of Otsu Thresholding and an equalizer. This helps in detecting pneumonia clouds and identifying the ratio of healthy lung region to the total region minimum. The above task is determined by importing the original chest x-ray images in the dataset and then calculating the ratio. The preprocessed data is then fed into Inception V3 model that accurately predicts the percentage of how much pneumonia is spread. This helps in identifying pneumonia present in that area and helps determining the prescribed drugs to help them clear off the symptoms.

scholarly journals IMPLEMENTASI TRANSFER LEARNING PADA CONVOLUTIONAL NEURAL NETWORK UNTUK DIAGNOSIS COVID-19 DAN PNEUMONIA PADA CITRA X-RAY

2021 ◽  
Vol 2 (1) ◽  
pp. 10-15
Author(s):  
Alexander Eric Wijaya ◽  
Windra Swastika ◽  
Oesman Hendra Kelana
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Model ◽  
Head Model ◽  
X Ray

COVID-19 menjadi salah satu masalah yang besar bagi banyak negara di dunia sejak tahun 2020. COVID-19 dan Pneumonia memiliki kemiripan dalam hal gejala seperti batuk dan sesak napas. Upaya diagnosis COVID-19 dan Pneumonia dilakukan dengan pemeriksaan laboratorium dan juga rontgen dada. Citra hasil x-ray dada pasien COVID-19 memiliki kesamaan dengan hasil x-ray pasien Pneumonia tetapi ahli radiologi berhasil menemukan bahwa terdapat perbedaan antara citra x-ray dada penderita COVID-19 dengan citra x-ray dada pasien Pneumonia dimana terdapat pola seperti kaca yang ditumbuk pada hasil citra X-ray penderita virus Corona.Diagnosis pada citra x-ray dada pasien menggunakan model Deep Learning. Pada penelitian ini juga akan membandingkan performa model Xception menggunakan Transfer Learning dengan performa model Xception tanpa Transfer Learning. Terdapat 4 eksperimen konfigurasi pada model Xception tanpa Transfer yaitu konfigurasi pelatihan layer base model, pelatihan base model, pelatihan custom head model, dan pelatihan pada layer base model serta custom head model. Terdapat 2 eksperimen menggunakan model Resnet50 dan VGG16 tanpa Transfer Learning. Model Xception menggunakan Transfer Learning memiliki performa lebih baik daripada model Xception tanpa Transfer Learning. Keempat eksperimen model Xception tanpa Transfer Learning dan kedua eksperimen dengan model Resnet serta VGG16 memiliki akurasi diatas 85%. Namun keenam model tanpa Transfer Learning tersebut tidak mampu mengenali Pneumonia pada citra x-ray dada pasien.

scholarly journals Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19

2021 ◽  
Vol 173 ◽  
pp. 114677
Author(s):  
Plácido L. Vidal ◽  
Joaquim de Moura ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Transfer Learning ◽  
Lung Segmentation ◽  
X Ray ◽  
Multi Stage

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

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