Pneumonia Detection and Classification Using Lightweight ShuffleNetV2 Based on Transfer Learning

Pneumonia, a severe respiratory infection, is commonly caused by either viral or bacterial pathogens. Timely identification of pneumonia is essential for effective patient care and treatment. Chest X-ray imaging is widely utilized for both diagnosing and detecting pneumonia, though interpreting these images presents certain difficulties. This research leverages transfer learning to evaluate and compare the performance of various convolutional neural networks (CNNs) in the context of chest X-ray image classification and detection. The results of the experiment demonstrate that the lightweight ShuffleNetV2 model excels in minimizing computational burden and memory consumption, while preserving high accuracy and effectiveness in classification tasks.