The Chest X-ray Classification project utilizes a carefully curated dataset comprising 5,863 high-quality X-ray images in JPEG format. The dataset is organized into three folders, namely train, test, and val, each containing subfolders for two distinct image categories: Pneumonia and Normal.
The primary objective of this project is to develop an accurate and efficient classification model capable of identifying pneumonia cases in patients based on chest X-ray images. Pneumonia is a prevalent and potentially life-threatening respiratory infection. Early detection plays a critical role in timely intervention and effective treatment.
By leveraging advanced Machine Learning Techniques, including Deep Neural Networks and Image Analysis Algorithms, the project aims to train a model that can precisely classify chest X-ray images into two categories: Pneumonia and Normal. The model will be trained on the "train" subset of the dataset and evaluated on the test and val subsets to ensure robust performance.
The outcomes of this research have significant implications for pediatric healthcare. An accurate and automated system for pneumonia detection in chest X-ray images can assist healthcare professionals in diagnosing and treating pneumonia cases promptly. This can potentially lead to improved patient outcomes, reduced hospital stays, and better allocation of medical resources.
Furthermore, the project contributes to the field of medical imaging and computer-aided diagnosis. The insights gained from this research can be applied to other medical imaging modalities and conditions, leading to advancements in automated disease detection and diagnosis.