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

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Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent

Mohsen Keshani¹, Zohreh Azimifar, Farshad Tajeripour

¹Department of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran. keshani.mhsn@yahoo.com

Computers in Biology and Medicine

|February 2, 2013

Summary

This study introduces a new method for detecting and segmenting lung nodules in CT scans using active contour modeling and support vector machines. The approach achieves an 89% detection rate for various nodule types.

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

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Published on: May 19, 2023

Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Radiology

Background:

Lung nodules are critical indicators of lung disease.
Accurate detection and segmentation of lung nodules in CT images remain challenging.

Purpose of the Study:

To present a novel, automated method for lung nodule detection, segmentation, and classification from CT images.
To improve the accuracy and efficiency of lung nodule analysis.

Main Methods:

Segmentation of lung area using active contour modeling and masking.
Nodule detection via Support Vector Machine (SVM) classifier with 2D/3D features.
Contour extraction and segmentation of solid and cavitary nodules.
Classification of lung tissues into four categories for nodule characterization.

Main Results:

Achieved an 89% overall detection rate for solid, non-solid, and cavitary nodules.
Reported 7.3 false positives per scan.
Successfully recognized the location of all detected nodules.
Demonstrated accurate segmentation of nodule contours.

Conclusions:

The proposed method offers a robust and accurate approach for lung nodule analysis in CT scans.
The technique effectively distinguishes between solitary and attached nodules.
Performance validated against clinical data and public datasets (LIDC, ANODE09).