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Related Concept Videos

Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Ultrasonography01:17

Ultrasonography

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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CT-ML: Diagnosis of Breast Cancer Based on Ultrasound Images and Time-Dependent Feature Extraction Methods Using

Behnam Hajipour Khire Masjidi1, Soufia Bahmani2, Fatemeh Sharifi3

  • 1Department of Computer Engineering, Islamic Azad University Tehran North Branch, Tehran, Iran.

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This study introduces a new feature extraction method using contourlet transforms for breast cancer diagnosis from ultrasound images. The decision tree model achieved the highest accuracy, demonstrating its effectiveness in classifying benign, malignant, and normal breast tissues.

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Biomedical Engineering

Background:

  • Breast cancer is a significant global health concern, particularly for women.
  • Accurate and efficient diagnosis of breast cancer is crucial for effective treatment.
  • Machine learning offers potential for improving breast cancer detection from medical images.

Purpose of the Study:

  • To develop an efficient feature extraction method for breast cancer diagnosis using ultrasound images.
  • To evaluate the performance of various machine learning classifiers for breast cancer classification.
  • To identify the most accurate classification approach for distinguishing between benign, malignant, and normal breast tissues.

Main Methods:

  • Utilized a two-dimensional contourlet transform for feature extraction from breast ultrasound images.
  • Modeled sub-banded contourlet coefficients using a time-dependent model to create feature vectors.
  • Applied and compared k-nearest neighbor, support vector machine, decision tree, random forest, and linear discrimination analysis for classification.

Main Results:

  • The decision tree classifier demonstrated high sensitivity: 87.8% for normal, 92.0% for benign, and 87.0% for malignant samples.
  • The proposed feature extraction method showed compatibility with the decision tree approach.
  • The decision tree architecture achieved the highest accuracy among the tested classification methods.

Conclusions:

  • The developed contourlet-based feature extraction method is effective for breast cancer diagnosis.
  • The decision tree classifier is a highly accurate and compatible tool for classifying breast cancer from ultrasound images.
  • This approach holds promise for improving the accuracy and efficiency of breast cancer detection.