Leveraging Advanced Visual Recognition Classifier For Pneumonia Prediction

Abstract

Pneumonia prediction using chest X-ray images is a challenging task because of the complex image processing involved. The radiographic features of pneumonia, especially in the earlier stages, easily overlap with other lung conditions, which makes the differentiation even more challenging. Moreover, X-ray image quality varies due to equipment, patient condition, and techniques, particularly in rural areas with undertrained radiologists and medical experts. The use of Artificial Intelligence (AI) models in detecting pneumonia is a novel but crucial research field and rapid advancement in medical imaging technology and neural network models along with the availability of large de-identified public datasets has paved the way for this life-saving biomedical research. In this paper, we propose a unique comprehensive solution for predicting pneumonia using chest X-ray images. We utilize an enhanced VGGNet model tailored for the binary classification task. The modified VGG19 with a binary classifier provides a solid foundation for feature extraction and leverages the pretrained features and deep architecture to differentiate between normal and pneumonia-affected lung images. The use of transfer learning allows us to extend the pre-trained model on a diverse and large-scale dataset by further training it on limited-size medical imaging data for the crucial task of biomedical classification without the need for large, labeled training data or computational resources. The robust model displays high accuracy of 92% with a high recall of 96.4% and AUC of 97%. With high adaptability and efficient learning capacity from limited data. This implementation may serve as a powerful tool assisting medical professionals in diagnosing pneumonia by quickly analyzing X-ray images with the same consistency and accuracy. During crises such as pandemics where lung diseases might surge, such tools can aid in rapid screening and monitoring of public health.