This paper proposes a hybrid deep learning model for lung abnormalities detection using X-ray images. To improve the performance and accuracy of the model, we use the transfer learning technique with two pre-trained models VGG16 and DenseNet121. Moreover, to extract deeply the feature of lung abnormal, frontal and lateral views of X-ray images have been trained using ensemble technique. The features extracted by these two models will be combined and passed to the classification layer. The experimental results on three datasets demonstrate the effectiveness of the proposed model, which outperforms the individual performance of the two base models, achieving a higher accuracy rate of 89%. Furthermore, in comparative assessments against several alternative models and datasets from previous research, our method demonstrates its efficiency, boasting an impressive AUC value of 0.95. These results underscore the promise of our approach in advancing the accuracy and effectiveness of lung abnormality detection in chest X-ray images.