Abstract:
"The urgent international health issue of accurately identifying pneumonia, a major cause of illness and death, is made worse by the inherent difficulties in interpreting chest X-ray pictures. Conventional diagnostic techniques frequently depend on the proficiency of radiologists, resulting in inconsistency in diagnosis due to the subjective nature of image analysis. The diversity highlights the urgent requirement for a sophisticated, automated method that can improve the accuracy and efficiency of diagnosing pneumonia using chest X-rays.
This research presents a new AI-based system for detecting pneumonia. This program uses advanced deep learning techniques to automatically analyze and segment chest X-ray pictures. The algorithm uses a carefully selected test dataset to evaluate its ability to segment by employing important metrics such as Intersection over Union (IoU), Dice Score, and Precision. The criteria were selected based on their significance in medical image segmentation tasks, ensuring a comprehensive evaluation of the algorithm's performance.
The preliminary test findings are encouraging, demonstrating the algorithm's strong effectiveness with an Intersection over Union (IoU) of 95.64, a Dice Score of 97.75, and a Precision of 98.12. These metrics indicate the algorithm's ability to reliably detect regions impacted by pneumonia. These results not only emphasize the algorithm's initial achievement but also its capacity to greatly enhance pneumonia detection, representing a crucial advancement in improving medical diagnostics through AI-driven approaches."
