Deep learning has revolutionized healthcare applications, particularly in the diagnosis, treatment, and management of infectious diseases. The main objectives of this investigation are to propose several methods for assessing high-resolution X-ray images with the purpose of identifying the occurrence or not of symptoms associated with pneumonia. The objective of this exam was to identify fixes for these existing problems. Our offering entails a deep learning (DL) technique for detecting chest anomalies using the X-ray modality using the EfficientNet B0 model. In order to make accurate diagnoses of pneumonia, both the EfficientNet B0 and the upgraded CNN model undergo extensive data-driven training. The CNN model that underwent upgrades was determined to be the most effective in this analysis because to its high level of accuracy. The results of our research conclusion are that DL models are capable of monitoring pneumonia's development, increasing diagnostic precision overall and giving patients new optimism for immediate relief.
Keywords
Deep Learning, EfficientNet, Classification, X-ray, Pneumonia.
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Sutha K
Sutha K
Department of Computer Science and Applications, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.
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Cite this article
Pavithra V, Uma Shankari Srinivasan, Sutha K, Saraswathi K, Mrutyunjaya S Yalawar and Sathiya B, “An Efficient Transfer Learning-Based Framework for Health Care Application”, Journal of Machine and Computing, pp. 1126-1139, October 2024. doi:10.53759/7669/jmc202404104.
