Enhanced deep transfer learning with multi-feature fusion for lung disease detection

Early detection of lung disease is important for timely intervention and treatment, enhancing patient outcomes and decreasing healthcare cost. Chest X-rays are a widely employed imaging modality to examine the structures within the chest, including the lungs and surrounding tissues. Lung disease detection using chest X-rays is a critical application of medical imaging and artificial intelligence (AI) in healthcare. Recently, lung disease detection using deep learning (DL) becomes a significant research area, which has the potential to improve early detection rate and decrease mortality rate. Therefore, this article introduces a Multi-Feature Fusion Based Deep Transfer Learning with Enhanced Dung Beetle Optimization Algorithm (MFFTL-EDBOA) for lung disease detection and classification. The MFFTL-EDBOA technique aims to recognize the existence of lung diseases on CXR images. At the primary stage, the MFFTL-EDBOA technique uses adaptive filtering (AF) approach to remove the noise level. Besides, a multi-feature fusion-based feature extraction approach is developed based on three DL models namely DenseNet, EfficientNet, and MobileNet. For accurate lung disease detection and classification purposes, the convolutional fuzzy neural network (CFNN) approach is utilized. The hyperparameter tuning of the CFNN model occurs using the EDBOA. To illustrate the enhanced lung disease detection results of the MFFTL-EDBOA technique, a sequence of experiments is carried out on benchmark medical dataset from Kaggle repository. The experimental values highlighted the greater result of the MFFTL-EDBOA system over other recent approaches with maximum accuracy of 98.99%.