Abstract:
Medical imaging plays an increasingly crucial role in radiology, facilitating accurate and efficient diagnosis of complex medical cases. One such case that requires atten...Show MoreMetadata
Abstract:
Medical imaging plays an increasingly crucial role in radiology, facilitating accurate and efficient diagnosis of complex medical cases. One such case that requires attention is aortic aneurysm, which often lacks noticeable symptoms and can result in sudden death if left undetected. In order to address this issue, we have developed a feature extractor block utilizing YOLOv5 and EfficientNet. This block effectively extracts and localizes thoracic aortic enlargement in chest radiography, enabling precise identification of the condition. Our research employs a two-stage training strategy, yielding commendable results for the abnormality and detection modules. The sensitivity and specificity achieved were 0.842 and 0.977, respectively. Additionally, the mean average precision at a threshold of 0.5 reached an average value of 0.884, while TAD (thoracic aortic enlargement) detection achieved a score of 0.994. Other metrics demonstrate favorable outcomes when compared to relevant literature. Consequently, this study represents a significant advancement towards the development of intelligent and efficient image retrieval systems, which have the potential to greatly impact the diagnosis of complex medical cases in the emergency department, specifically those related to TAD.
Published in: 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)
Date of Conference: 20-23 September 2023
Date Added to IEEE Xplore: 22 November 2023
ISBN Information: