Abstract
The diagnosis is one of the best solution for finding the health problems and Inaccurate diagnosis of pneumonia might lead to serious health problems. For diagnosis, traditional chest X-rays are used; however, manual interpretation is laborious and prone to human mistake. Therefore, we have created a powerful deep learning method that allows for automatic pneumonia identification using chest radiographs. BeginningCNN models that are now in use include ResNetV2, ResNet50, VGG16, EfficientNetV2L, Xception, and NasNetMobile. We first integrate Xception and NasNetMobile to facilitate classification. Next, we emphasize the sites of irregularities in chest pictures using object identification techniques from YOLOv5x6, YOLOv5s6, YOLOv8n, and YOLOv9n. The proposed framework achieves an accuracy of 91.75%, surpassing several industry standards such as COVID-Net (87.00%), DenseNet121 (84.00%), and CheXNet (76.80%). The diagnostic model's claimed precision of 92.30%, recall of 91.10%, F1-score of 91.70%, and AUC of 0.935 demonstrate its balance and high reliability. This combination categorization and detection technology not only improves diagnostic accuracy but also speeds up and improves the decision-making process for doctors.
