The objective of this project is to develop and evaluate machine learning models for the identification of pneumonia from chest X-ray images and to assess the potential generalizability of these models to other lung diseases. This involves leveraging image classification techniques—a domain where neural networks excel—by employing both fine-tuned pre-trained models and custom-developed models.

The project aims to conduct a comparative analysis of these models to identify the most effective approach. Given the binary classification nature of the task, distinguishing between "healthy" and "diseased" states, we aim to explore the models' applicability beyond pneumonia to other lung conditions.