Version 1
: Received: 26 April 2024 / Approved: 27 April 2024 / Online: 28 April 2024 (07:57:52 CEST)
How to cite:
Doğan, K.; SELÇUK, T.; ALKAN, A. An Improved Machine Learning Model for Pulmonary Embolism Detection and Segmentation. Preprints2024, 2024041810. https://doi.org/10.20944/preprints202404.1810.v1
Doğan, K.; SELÇUK, T.; ALKAN, A. An Improved Machine Learning Model for Pulmonary Embolism Detection and Segmentation. Preprints 2024, 2024041810. https://doi.org/10.20944/preprints202404.1810.v1
Doğan, K.; SELÇUK, T.; ALKAN, A. An Improved Machine Learning Model for Pulmonary Embolism Detection and Segmentation. Preprints2024, 2024041810. https://doi.org/10.20944/preprints202404.1810.v1
APA Style
Doğan, K., SELÇUK, T., & ALKAN, A. (2024). An Improved Machine Learning Model for Pulmonary Embolism Detection and Segmentation. Preprints. https://doi.org/10.20944/preprints202404.1810.v1
Chicago/Turabian Style
Doğan, K., Turab SELÇUK and Ahmet ALKAN. 2024 "An Improved Machine Learning Model for Pulmonary Embolism Detection and Segmentation" Preprints. https://doi.org/10.20944/preprints202404.1810.v1
Abstract
Pulmonary Embolism (PE) is the obstruction of blood arteries in the lungs by a blood clot. The mortality risk for PE is approximately 30%. Detecting pulmonary embolism in the segmental arteries of the lung is more challenging than in the main arteries and is very prone to being missed. A computer-based approach was created in this work to automatically identify pulmonary embolism in the segmental arteries using computed tomography images. The system's infrastructure includes an enhanced Mask R-CNN deep neural network that has been trained with images containing PE. The network accurately identifies the location of the pulmonary embolism on the computed tomography picture and successfully extracts its borders. The investigation was conducted by generating a local dataset. The study's performance was evaluated using pulmonary embolisms identified manually by the expert radiologist. The sensitivity, specificity, accuracy, dice coefficient, and Jaccard index values for 2130 pictures were 96.4%, 93.5%, 96.1%, 0.96, and 0.90, respectively. The improved Mask R-CNN model has demonstrated superior performance when compared to the traditional Mask R-CNN and U-Net models. This high-performance technology is designed to identify pulmonary embolism and will aid professionals in assessing the size of the embolism.
Keywords
Pulmonary Embolism; Mask R-CNN; CTPA images
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
