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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

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An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique.

Fayez Eid Alazemi¹, Babar Jehangir², Muhammad Imran²

¹Department of Computer Science and Information Systems, College of Business Studies, The Public Authority for Applied Education & Training, Adailiyah 12062, Kuwait.

Journal of Healthcare Engineering

|February 14, 2023

Summary

This study introduces a new computer-aided detection (CAD) method to improve early lung cancer nodule identification in CT scans. The advanced model significantly reduces false positives, enhancing diagnostic accuracy for lung nodules.

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Lung cancer has the highest global mortality rate.
Early detection of lung nodules is crucial for improving patient survival rates.
Computed tomography (CT) imaging is a primary tool for lung cancer screening.

Purpose of the Study:

To present an improved computer-aided detection (CAD) method for lung nodules in CT images.
To provide an overview of current technologies in biomedical data processing for lung cancer detection.
To develop a robust model for accurate segmentation and classification of lung nodules.

Main Methods:

A three-step model was developed for lung nodule detection.
Lung segmentation was performed using thresholding and component labeling.
Nodule candidates were identified and segmented using optimal thresholding and rule-based trimming, followed by feature extraction (2D/3D).
Support Vector Machine (SVM) was trained using extracted features for nodule classification.

Main Results:

The proposed framework was evaluated on the LIDC dataset.
The method achieved a significant reduction in false positives, down to 4 FP per scan.
A high sensitivity of 95% was attained in nodule detection.

Conclusions:

The developed CAD method effectively improves the detection of lung nodules in CT images.
The proposed approach enhances diagnostic accuracy by reducing false positives and maintaining high sensitivity.
This technology holds promise for earlier and more reliable lung cancer diagnosis.