CHEST X-RAY BASED DISEASE ANALYSIS USING CNN

Machine Learning

The paper is centered around advancing medical diagnostics through the utilization of chest X-ray scans for the detection of respiratory and cardiac conditions. The primary focus is on developing and implementing a machine learning-based system for automated disease detection in chest X-ray images. The principal objective of this paper is to develop a resilient and precise system that can autonomously detect and categorize anomalies, specifically pneumonia, in chest X-ray scans. This is obtained and accomplished through the application of machine learning algorithms, including sophisticated techniques like Convolutional Neural Networks (CNNs). These algorithms analyze radiological images to recognize and categorize pathological conditions, contributing to more efficient and precise medical diagnostics. Key components of the paper include data collection, model development, and performance evaluation. In the data collection phase, a diverse dataset of chest X-ray images is compiled, ensuring comprehensive coverage of labeled disease conditions to encompass a wide spectrum of cases. The model development phase explores various machine learning architectures to construct an accurate disease detection model, with an emphasis on leveraging CNNs for their efficacy in image analysis. Performance evaluation is conducted using standard medical imaging metrics, including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC-ROC). These metrics provide a comprehensive assessment of the system's ability to correctly identify and classify diseases in chest X-ray scans. In addition to the technical aspects, the paper aims to enhance user interaction by creating a user-friendly interface tailored for radiologists and healthcare practitioners. This interface facilitates automated preliminary analysis, expediting the diagnostic process and supporting healthcare professionals in making quicker and more informed decisions. By integrating cutting-edge machine learning techniques, robust data collection, and user-friendly interface design, this paper strives to contribute significantly to the field of medical diagnosis, particularly in the automated detection of respiratory and cardiac conditions from chest X-ray images.

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