Detection of Pneumonia Using Convolutional Neural Networks

Pneumonia is a grave respiratory ailment that presents noteworthy health hazards, especially to susceptible groups like the elderly and small children.Pneumonia must be detected as soon as possible in order to receive treatment promptly and improve patient outcomes.The use of convolutional neural networks (CNNs) for the automatic diagnosis of pneumonia from chest X-ray pictures is investigated in this paper.We created a CNNbased model that uses deep learning's potent feature extraction capabilities to accurately diagnose pneumonia.Using a publicly accessible dataset of labeled chest X-ray pictures of both healthy and pneumonia cases, the model was trained and assessed.We tested with several CNN architectures, such as VGG16, ResNet50, and InceptionV3, to find the best model for this challenge.Our results show that the CNN-based method outperforms conventional machine learning techniques in terms of accuracy rates when it comes to differentiating between lungs with pneumonia and healthy lungs.Metrics including accuracy, sensitivity, specificity, and the area under the Receiver Operating Characteristic (ROC) curve are used to assess how useful the model.