Abstract:

A deep learning model titled the Pneumonia Affected Lung Identification using Deep learning aims to correctly identify pneumonia in chest X-ray images. The model makes use of the VGG16 architecture, a well-known convolutional neural network that performs admirably on tasks involving picture classification. Eight thousand of chest X-ray images with labels indicating the presence or absence of pneumonia along with the type of Pneumonia make up the dataset used for training and testing. The pre-trained VGG16 model was adjusted on the pneumonia dataset during the model's transfer learning training [1]. The outcomes show that the model had a high level of success in diagnosing pneumonia from chest X-ray pictures. Precision, recall, and F1 score were just a few of the evaluation criteria that were used to assess the model's performance. The outcomes show that the model can identify pneumonia using high accuracy and has the potential to assist healthcare professionals in diagnosing the disease

Description:

VGG16, deep learning, transfer learning, accuracy, evaluation metrics, precision, recall, F1 score