Pneumonia has been found to be the leading cause of death in young children. Early discovery would make it easier to receive treatment quickly, potentially saving many lives. In this research, convolutional neural networks and multilayer perceptrons will be compared and a method for automating the identification of pneumonia using chest X-rays will be developed. Kaggle applied custom convolutional neural networks and multi-layer perceptrons on a chest X-ray dataset. A GUI has been developed that accepts a chest X-ray, forecasts the presence of pneumonia, and displays the proportion of congestion. Convolutional neural network and multilayer perceptron model accuracy will be good, respectively. The GUI that was created produces positive results. Convolutional neural networks outperformed multi-layer perceptrons in terms of performance. Consequently, the GUI was created using a bespoke convolutional neural network. final year project is based on IEEE Paper.this will be one of the best Final year engineering project for computer science. Components that we will provide are.
1.complete documentation support
2.complete working hardware/software implemented in students environment
3.classes will held accordingly.