Lung diseases identification using hybrid transfer learning and bidirectional long short-term memory - Eka Cahyani, Denis; Tri Oktoviana, Lucky; Yasin, Mohamad; Wahyuningsih, Sapti; Dionixius, Dionixius; Maulidaningsih, Ranti; Setumin, Samsul (Institute of Advanced Engineering and Science, 2025)
Lung diseases rank as the third most prevalent cause of mortality globally. Accurate identification of lung disease is essential to provide appropriate medical intervention for patients. This research devised a categorization system for lung diseases using chest X-Rays (CXR). The system can identify bacterial pneumonia, viral pneumonia, COVID-19, tuberculosis, and normal CXR. The approach for detecting lung diseases utilize a combination of hybrid transfer learning and bidirectional long short-term memory. The research included convolutional neural network (CNN) models including Resnet50-BiLSTM, VGG19-BiLSTM, InceptionV3-BiLSTM, Resnet50, VGG19, and InceptionV3. The Resnet50-BiLSTM model outperforms other models in terms of accuracy and overall performance. The Resnet50-BiLSTM model achieved an accuracy of 99.87%. The models that achieve the second greatest accuracy are Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, and InceptionV3. The research utilizes precision, recall, and F1-Measure to demonstrate that Resnet50-BiLSTM outperforms other methods by achieving the greatest value. This research improves the performance outcomes when compared to earlier studies.