A deep learning approach for classification of COVID and pneumonia using DenseNet‐201. Issue 1 (29th September 2022)

• Record Type: Journal Article
• Authors: Sanghvi, Harshal A.; Patel, Riki H.; Agarwal, Ankur; Gupta, Shailesh; Sawhney, Vivek; Pandya, Abhijit S.
• Is Part Of: International journal of imaging systems and technology. Volume 33:Issue 1(2023)
• Page Start: 18
• Page End: 38
• Publication Date: 2022-09-29
• Subjects: bio‐medical innovation -- CNN classification -- COVID detection -- deep learning -- medical imaging -- public health information (PHI) -- transfer learning -- X‐ray imaging; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22812
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: 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.