A Comparative Study of Deep Learning Models for Detecting Pulmonary Embolism

Varshney, Aditya; Bansal, Arnav; Agarwal, Anshuman; Mishra, Vipul Kumar; Badal, Tapas

doi:10.1007/978-3-030-95502-1_7

Cited by 1 publication

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The training of classification models for medical pictures is conducted using extensive public health datasets. The resultant models possess the capability to be applied to patient test outcomes [13], enabling the identification of medical disorders and the automated generation of a prognosis.…”

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

A Comparative Study of Deep Learning Models for Detecting Pulmonary Embolism

Cited by 1 publication

References 20 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

Contact Info

Product

Resources

About