Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks

Arifin, Fatchul; Artanto, Herjuna; Nurhasanah, Nurhasanah; Gunawan, Teddy Surya

doi:10.35741/issn.0258-2724.56.2.19

Cited by 13 publications

(13 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The proposed MFDNN model is trained and tested on a public dataset (COVID19 chest X-ray dataset). The data set consists of 3616 COVID19 positive cases, 10,192 normal, 6012 lung opaque (non-COVID lung infection) and 1345 viral pneumonia images [ 24 , 25 ]. The dataset can be downloaded from the website [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images

Pan

Wang

et al. 2022

Health Inf Sci Syst

View full text Add to dashboard Cite

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID19) is life-saving important for both patients and doctors. This research proposes a multi-channel feature deep neural network (MFDNN) algorithm to screen people infected with COVID19. The algorithm integrates data over-sampling technology and MFDNN model to carry out the training. The oversampling technique reduces the deviation of the prior probability of the MFDNN algorithm on unbalanced data. Multi-channel feature fusion technology improves the efficiency of feature extraction and the accuracy of model diagnosis. In the experiment, Compared with traditional deep learning models (VGG19, GoogLeNet, Resnet50, Desnet201), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Furthermore, through ablation experiments, we proved that a multi-channel convolutional neural network (CNN) is superior to single-channel CNN, additional layer and PSN module, and indirectly proved the sufficiency and necessity of each step of the MFDNN classification method. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19 .

show abstract

Section: Methodsmentioning

confidence: 99%

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images

Pan

Wang

et al. 2022

Health Inf Sci Syst

View full text Add to dashboard Cite

show abstract

“…Both models have been able to detect the positive infection of COVID-19, negative cases (normal), and viral pneumonia with 93.24% accuracy. V1 model provides COVID-19 ability detection with 83.7% average accuracy, whereas the V2 model can achieve 87.5% average accuracy of COVID-19 detection [14]. Frikha Hounaida et al (2022) proposed a method for exploiting the variations of the ST segment perceived on recordings of the ECG signal.…”

Section: Related Workmentioning

confidence: 99%

Employing Transfer Learning for Diagnosing COVID-19 Disease

et al. 2022

View full text Add to dashboard Cite

Corona virus’s correct and accurate diagnosis is the most important reason for contributing to the treatment of this disease. Radiography is one of the simplest methods to detect virus infection. In this research, a method has been proposed that can diagnose disease based on radiography (X-ray chest) and deep learning techniques. We conducted a comparative study by using three diagnosis models; the first one was developed by using traditional CNN, while the two others are our proposed models (second and third models). The proposed models can diagnose the COVID-19 infection, normal cases, lung opacity, and Viral Pneumonia according to the four categories in the covid19 radiography dataset. The transfer learning technology had used to increase the robustness and reliability of our model, also, data augmentation was used for reducing the overfitting and to increase the accuracy of the model by scaling rotation, zooming, and translation. The third model showed higher training accuracy of 93.18% compared to the two other models that are dependent on using traditional convolution neural networks with an accuracy of 70.28% of the first model, while the accuracy of the second model that uses data augmentation with traditional convolution neural is 90.1%, while the testing accuracy models was 68.27% for the first model, 87.55% for the second model, and 86.03% for the third model.

show abstract

“…Kedua fase ini dilakukan menggunakan MobileNet-SSD yang telah di-load oleh modul DNN OpenCV tanpa harus membangun jaringan neural network sendiri. MobileNet-SSD mendeteksi objek dalam citra dengan membuat kotak pembatas (bounding box) dan pelabelan (ID) [12], yang selanjutnya diproses pada sistem pelacakan.…”

Section: Metode Penelitianunclassified

Penghitung Jumlah Pengunjung Objek Wisata Dengan Metode Deep Learning MobileNet-SSD

Saputra

Erwanto²,

Rizal³

2022

tech

View full text Add to dashboard Cite

Pada kondisi pandemi Covid-19, pengelola objek-objek wisata melakukan pembatasan jumlah pengunjung dengan penghitungan jumlah pengunjung yang masuk secara manual, yaitu dilakukan oleh pegawai/karyawan dengan menggunakan alat counter manual atau melalui penghitungan tiket sehingga sangat memungkinkan terjadi kesalahan dalam penghitungan jumlah pengunjung. Penelitian ini membuat sistem penghitungan jumlah pengunjung dengan memasang sebuah webcam. Webcam tersebut dapat dipasang pada pintu masuk maupun pintu keluar objek wisata. Metode yang digunakan pada penelitian ini adalah pemanfaatan model pra-terlatih MobileNet-SSD sebagai model deep learning. Dari penelitian yang dilakukan, MobileNet-SSD mampu mendeteksi jumlah pengunjung yang keluar dan masuk sehingga dapat menghitung jumlah pengunjung dan melakukan pembatasan jumlah pengunjung pada objek wisata.

show abstract

Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks

Cited by 13 publications

References 1 publication

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images

Employing Transfer Learning for Diagnosing COVID-19 Disease

Penghitung Jumlah Pengunjung Objek Wisata Dengan Metode Deep Learning MobileNet-SSD

Contact Info

Product

Resources

About