Pneumonia Detection Technique Empowered with Transfer Learning Approach

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Pneumonia Detection Technique Empowered with Transfer Learning Approach

Journal Title: Healthcraft Frontiers - Year 2024, Vol 2, Issue 1

Abstract

Detection of normal findings or pneumonia using modern technology has a lot of significance in medical analysis and artificial intelligence. Still, more specifically, its importance increases in deep learning. Deep learning is extensively applied in the realm of medicine and disease classification. Early diagnosis of pneumonia is essential so it can be efficiently treated with the type of antibiotics. Bacterium and viruses are the population's first cause of pneumonia and death. Bacteria and viruses are part of mammalian pathogens and the most invasive type of bacteria or virus causing many diseases. Bacterial infection is among the most common types of disease in all age groups, but most bacterial infectious diseases are not the same. Our research will propose a transfer learning-based approach for pneumonia prediction utilizing a dataset comprising chest X-ray images. The dataset-based images will be grouped into two groups based on the parameters. Our proposed model displayed an average accuracy of 94.54% on the dataset. The proposed model (PDTLA) performed well compared with previous quantitative and qualitative research studies. Pneumonia detection transfer learning algorithm (PDTLA) is the name of the modified model.

Authors and Affiliations

Muhammad Daniyal Baig, Hafiz Burhan Ul Haq, Muhammad Nauman Irshad, Waseem Akram

Keywords

Convolutional neural network (CNN); Deep learning; Pneumonia; Medical imagining

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EP ID EP744644
DOI https://doi.org/10.56578/hf020103
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