The project aimed to develop a deep-learning model to diagnose pneumonia from chest X-rays, targeting an accuracy of 90%. Through iterative experiments with various CNN architectures, including custom models and state-of-the-art networks like NASNet, the final model achieved 92.78% accuracy. This demonstrates AI's potential to enhance diagnostic accuracy and speed, particularly in high-pressure clinical settings. The project contributes to the integration of AI in healthcare, offering a reliable tool to support medical professionals.

The primary goal of the project was to develop a deep-learning model capable of accurately diagnosing pneumonia from chest X-rays using image classification techniques. The target was to create a model with an accuracy of at least 85.8%, ideally reaching 90%, which would surpass the diagnostic accuracy of many medical professionals. This project aimed to demonstrate the potential of AI in assisting with quick and accurate diagnosis in clinical settings, particularly in high-pressure environments like emergency departments.

The solution involved a series of iterative experiments with different convolutional neural network (CNN) architectures and techniques, including custom CNN models and state-of-the-art architectures like EfficientNet and NASNet. The development process included extensive training, fine-tuning, and optimization using methods like transfer learning, dropout layers, and data augmentation. The final model achieved an accuracy of 92.78%, exceeding the project's goal.

The project demonstrated that AI could significantly improve the accuracy and speed of pneumonia diagnosis from chest X-rays. By achieving an accuracy rate higher than many medical professionals, the solution highlights AI's potential to support healthcare providers, reduce diagnostic errors, and enhance patient outcomes. This work contributes to the broader field of AI in healthcare, showcasing how machine learning can be leveraged to address critical medical challenges, particularly in resource-constrained environments.