Deep learning

Electrical engineering

Internal medicine

Radiology

Transfer of learning

Health Insurance Portability and Accountability Act

Radiography

Confidentiality

Operating system

World Wide Web

Engineering

Artificial intelligence

Technician

Software

Pneumonia

Computer science

Chest radiograph

Computer security

Software portability

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Medicine

International Journal of Imaging Systems and Technology

In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X‐ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X‐ray Image. The software then uploads chest X‐ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio‐Medical Innovation is a user‐ready framework which assists the medical providers in providing the patients with the best‐suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care.

A deep learning approach for classification of <scp>COVID</scp> and pneumonia using <scp>DenseNet</scp>‐201

A deep learning approach for classification of COVID and pneumonia using DenseNet‐201