Deep Learning Model to Predict Pneumonia Disease based on Observed Patterns in Lung X-rays

Singh, Utkarsh; Totla, Aditi; Kumar, Prakhar

doi:10.1109/iceca49313.2020.9297388

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A handful of work was done involving the use of pretrained models for the task of identifying lung diseases from the x-ray or CT images. [19][20][21][22][23] A recommendation of a super-resolution framework was put forward for enhanced nodule detection. Wu et al 24 gave a deep residual network framework in which ResNet50 was taken as a base model for classification on the LIDC-IDRI database.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Domain adaptation and weight initialization of neural networks for diagnosing interstitial lung diseases

Thorat

Salvi

Dedhia

et al. 2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Interstitial lung diseases (ILDs) are adverse disorders, damaging the lung tissues, thus making timely diagnosis imperative. To counter the scarcity of publicly available high‐resolution computed tomography (HRCT) data, architecture employing HRCT and x‐ray data have been proposed for diagnosing ILDs. A model is first trained to diagnose three ILDs and a healthy lung from x‐ray data and then with a small amount of HRCT data of the same four classes. We introduce an EfficientNet+AlexNet model and a custom deep convolutional neural network and compare their performances with different weight initialization at the start of x‐ray image training. The EfficientNet+AlexNet model initialized with ImageNet weights performed best on the x‐ray dataset and was later used for domain adaptation. We also compare our model's performance to a single pre‐trained EfficientNetB0 model (trained on the same HRCT data). Our model gave an accuracy of 97.4% on the testing dataset, a notable increase in the performance compared to the recently proposed methodologies. The experimental results and graphical plots depict the superiority of domain adaptation and the potentials of weights that are a starting point for training the models.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…Correlation learning mechanism (CLM) has been explored for brain tumor detection from CT images in 18 due to faster learning and parallel implementation. A handful of work was done involving the use of pre‐trained models for the task of identifying lung diseases from the x‐ray or CT images 19–23 …”

Section: Literature Reviewmentioning

confidence: 99%