A comprehensive Analysis on Diagnosis of Pneumothorax Disease Using Convolutional Neural Network and Data Visualization for Detection

This research paper explores the use of data visualization techniques to analyze a dataset related to pneumothorax, a medical condition characterized by air in the pleural cavity. The dataset consists of medical images in DICOM format, with labels indicating the presence or absence of pneumothorax. The study preprocesses the data, handles missing values, and explores key attributes such as patient demographics, imaging modalities, and view positions. Visualizations are created to illustrate the distribution of patients’ age, gender, and imaging characteristics. The occurrence of pneumothorax across different view positions is analyzed. The findings highlight the effectiveness of data visualization in gaining insights into complex medical datasets, aiding in clinical decision-making and research efforts related to pneumothorax diagnosis and treatment. The paper also provides an overview of convolutional neural network (CNN) models used in pneumothorax detection, discussing various architectures, their application in medical imaging data, and their evolution over time. The insights gained from this analysis contribute to understanding the effectiveness of CNNs in pneumothorax detection and their potential impact on clinical practice and healthcare outcomes.