Segmentation of Pulmonary Embolism Using Deep Learning

Yadlapalli, Priyanka; Teja, A Love; Raju, C Mohan Aditya; Reddy, K Samara Simha; Mithra, Krishna; Dokku, Bhavana

doi:10.1109/iconat53423.2022.9726048

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to analyse data and come to conclusions that are comparable to those of humans, deep learning algorithms employ a predetermined logical structure. Deep learning with neural networks is used to achieve this [6,7]. The basic diagram of deep learning is as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Architecture Strategies for the Prediction of Acute Pulmonary Embolism from Computed Tomography Images

Yadlapalli,

2024

SCPE

View full text Add to dashboard Cite

The timely identification of pulmonary embolism is of utmost importance, as the condition has the potential to be life-threatening if not promptly addressed. The assessment of the severity of a pulmonary embolism (PE) frequently necessitates a time-consuming and potentially life-threatening estimation by a medical practitioner. The primary objective of the study was to investigate the potential utility of an artificial neural network in assisting physicians with the identification and prediction of pulmonary embolism risk in patients. Deep learning algorithms are frequently employed in medical imaging to enhance image analysis due to their ability to automatically learn representations from large datasets, as opposed to relying on pre-programmed instructions . The implementation of automated systems has the potential to decrease the level of physical effort needed and enhance the efficiency of diagnostic procedures for medical professionals. Efficient training and calculation processes are crucial for the proper execution of the implementation. The Tensor Processing Units (TPUs) developed by Google are employed to expedite the process of training., with the computational tasks being executed through Google Colab, a platform offered by Google Cloud TPUs. In order to achieve outcomes comparable to human judgment, deep learning algorithms engage in reasonable assessments of data based on a predetermined logical framework. Diagnosing pulmonary embolism (PE), a potentially lethal yet curable condition, poses challenges in early detection. A distinctive convolutional neural network (CNN) model was developed and examined for the purpose of distinguishing between pulmonary embolism (PE) and computed tomography (CT) pictures. The proposed study yields a precision rate of 91.2%, showcasing an enhancement compared to current convolutional neural network (CNN) architectures that include limited trainable parameters. Furthermore, our model provides interpretability by the utilization of computed tomography (CT) images, specifically in the inferno and bone models. Our proposed deep learning model has the potential to predict the presence of PE and other associated features in current cases.

show abstract

Section: Introductionmentioning

confidence: 99%

Hybrid Architecture Strategies for the Prediction of Acute Pulmonary Embolism from Computed Tomography Images

Yadlapalli,

2024

SCPE

View full text Add to dashboard Cite

show abstract

Pixel-level annotated dataset of computed tomography angiography images of acute pulmonary embolism

et al. 2023

View full text Add to dashboard Cite

Pulmonary embolism has a high incidence and mortality, especially if undiagnosed. The examination of choice for diagnosing the disease is computed tomography pulmonary angiography. As many factors can lead to misinterpretations and diagnostic errors, different groups are utilizing deep learning methods to help improve this process. The diagnostic accuracy of these methods tends to increase by augmenting the training dataset. Deep learning methods can potentially benefit from the use of images acquired with devices from different vendors. To the best of our knowledge, we have developed the first public dataset annotated at the pixel and image levels and the first pixel-level annotated dataset to contain examinations performed with equipment from Toshiba and GE. This dataset includes 40 examinations, half performed with each piece of equipment, representing samples from two medical services. We also included measurements related to the cardiac and circulatory consequences of pulmonary embolism. We encourage the use of this dataset to develop, evaluate and compare the performance of new AI algorithms designed to diagnose PE.

show abstract

Segmentation of Pulmonary Embolism Using Deep Learning

Cited by 2 publications

References 8 publications

Hybrid Architecture Strategies for the Prediction of Acute Pulmonary Embolism from Computed Tomography Images

Hybrid Architecture Strategies for the Prediction of Acute Pulmonary Embolism from Computed Tomography Images

Pixel-level annotated dataset of computed tomography angiography images of acute pulmonary embolism

Contact Info

Product

Resources

About