Influenza Pneumonia an Efficient Deep Learning Based Classification to Differentiate Coronavirus and Influenza Pneumonia

Abstract

In the context of increased numbers of coronavirus-affected people, the availability of highly accurate detection of COVID-19 is a crucial task worldwide due to its structural changes and feature similarities with influenza pneumonia. RT-PCR (Reverse Transcription Polymerase Chain Reaction), a molecular test frequently used for virus detection, is a diagnostic tool for coronavirus and influenza pneumonia. Two subtypes of influenza pneumonia (IP), known as types A and B, share biological characteristics with coronaviruses. The medical findings might not be able to distinguish between the novel coronavirus and numerous types of influenza pneumonia due to coronavirus variants. Coronavirus is detected using real-time reverse transcription polymerase chain reaction, but this method has a significant negative false positive rate. A medical specialist's help is required for the early and correct detection of coronavirus and influenza pneumonia to treat and recover patients from infections following their modality.  Coronavirus and influenza pneumonia have similar illness patterns, and no diagnostics have a high positive rate. The precise diagnosis of these infections is difficult for medical professionals. For the disease to be identified and treated promptly to prevent further health problems, precise COVID-19 and influenza pneumonia detection is necessary. Therefore, our proposed work in this thesis focuses on the high-quality evaluation and detection of both viruses and answers the following questions:

• How can early recognition of COVID-19 and influenza pneumonia be evaluated through medical images?


• How can the quality of results regarding the early recognition of COVID-19 and influenza pneumonia be enhanced?

The above questions will be answered by deep learning state-of-the-art models and evaluation metrics for early detection of the viruses. This thesis presents precise virus detection through the proposed model with enhanced results. The medical images are evaluated using the deep learning-based model VGG-8. Our research used the database of X-ray medical images. X-ray images comprise the coronavirus, pneumonia and normal ordinary people, which are used for the classification through deep learning model. Evaluation of X-ray medical data shows the model’s accuracy to up to 98.99% for classification. Therefore, the proposed evaluation system and classification model successfully detected the coronavirus and influenza pneumonia with improved results.