An efficient deep learning approach for detecting lung disease from chest x-ray images using transfer learning and ensemble modeling

Abstract

Among the most convenient bacteriological assessments for the diagnosis and treatment of several health complications is the chest X-Ray. In X-Ray imaging, it is a common technique to standardize the extracted image reconstruction with the usual uniform disciplines taken before the study. Unfortunately, there has been relatively little study on several separate lung disease monitoring, including X-Ray picture analysis and poorly labelled repositories. Our paper suggests an effective automated approach for the detection of lung disease trained on chest X-ray images. Besides, with a weighted binary classifier, a particular technique is also deployed that will optimally leverage the weighted predictions from optimal deep neural networks such as Inception-v3, VGG16 and ResNet-50. In addition to the existing, transfer learning, along with more rigorous academic training and testing sets, is used to fine-tune deep neural networks to achieve higher internal processes. In comparison, 88.14 percent test accuracy was obtained with the final proposed weighted binary classifier, where other models give us about 80.9 percent average accuracy. For a brief recurring diagnosis, the legally prescribed procedure may also be used which may increase the course of the same condition for physicians. For a prompt diagnosis of pneumonia, the suggested approach should be used and can improve the diagnosis process for health practitioners.