Abstract:
Pneumonia is the leading cause of death among young children, highlighting the importance of early detection for prompt treatment and potentially saving numerous lives. This research aims to compare convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) to develop an automated method for pneumonia identification using chest X-rays. Kaggle's custom CNNs and MLPs were applied to a dataset of chest X-rays. A user-friendly graphical user interface (GUI) was developed to accept chest X-rays, predict pneumonia presence, and display congestion levels. The accuracy of the CNN and MLP models was found to be good, with the GUI yielding positive results. CNNs outperformed MLPs in terms of performance, leading to the implementation of a custom CNN in the GUI. This final-year engineering project, based on an IEEE paper, holds immense potential for computer science students. The components we provide include:
1.complete documentation support
2.complete working hardware/software implemented in students environment
3.classes will held accordingly.
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