AI-Powered Pneumonia Detection: Enhanced Chest X-ray Interpretation with CNNs

The nearly lethal bacterial disease known as pneumonia, caused by the streptococcus pneumoniae bacterium, can infect any individual at any time. It often affects one or both lungs. One out of every three deaths in India is attributed to pneumonia, according to the World Health Organisation (WHO). In order to diagnose pneumonia, chest X-rays must be reviewed by trained radiologists. In order to treat pneumonia as quickly as possible, especially in faraway regions, it would be beneficial to have an automated method to detect the disease. The success of deep learning algorithms in analysing medical images has piqued a lot of people's interest in using Convolutional Neural Networks (CNNs) for illness detection. The proposed CNN-based method has several advantages over more traditional approaches, including greater accuracy, scalability, and the possibility of automation. Our proposal aims to improve the accuracy of pneumonia diagnoses by using deep learning techniques to identify complex patterns and traits in medical imaging data. Furthermore, features extracted from large datasets using pre-trained convolutional neural network (CNN) models perform quite well in image classification tasks. In this study, we evaluate these models’ capacity to distinguish between normal and pathological chest X-rays by employing pre-trained convolutional neural network (CNN) models as feature extractors and a number of classifiers thereafter. Analytical methods are employed to determine which CNN model is most suitable for the job at hand.