Efficient Classification Model of Pneumonia Infection Based on Deep Transfer Learning and Chest X-Ray Images
Chest X-ray radiographic (CXR) imaging facilitates the early and precise diagnosis of lung disease. Combining CXR imaging with CNN and other artificial intelligence tools can expedite the diagnosis process. Because there are only a small number of clinical images that have been categorized, the most difficult challenge to overcome before these images can be reliably used in disease progression diagnosis is the automated classification of these images as positive or negative cases. A deep-learning approach was proposed to classify lung diseases on CXR images using a transfer learning technique based on the EfficientNetV2 model as the backbone to boost computer-aided diagnosis (CAD) performance reliability and efficiency. The proposed model is trained and tested on the two classifications, normal and pneumonia, based on images from three publicly available chest X-ray datasets. Using the curated dataset of posteroanterior (PA) chest view radiography images, the implemented model achieves an outstanding performance on the tested data represented by an average accuracy of 99.92%. Consequently, it outperforms the most recent classification techniques de-scribed in the literature.
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