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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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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Ultrasonography01:17

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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.
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Related Experiment Video

Updated: Jan 18, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Multi-Class Classification of Breast Ultrasound Images Using Vision Transformer-Based Ensemble Learning.

Tuğçe Taşar Yıldırım1, Orhan Yaman2, İrfan Kılıç3

  • 1Department of Internal Medicine, Fethi Sekin City Hospital, Elazig 23300, Türkiye.

Diagnostics (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

A new vision transformer (ViT) ensemble model accurately classifies breast ultrasound images. This AI approach enhances diagnostic accuracy for normal, benign, and malignant tumors, aiding clinical decisions.

Keywords:
breast ultrasound imagesensemble learningfocal lossmedical image classificationregion of interest (ROI) segmentationvision transformer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Breast ultrasound imaging is crucial for disease detection.
  • Accurate classification of normal, benign, and malignant breast lesions is essential.
  • Existing methods may have limitations in accuracy and explainability.

Purpose of the Study:

  • To develop a vision transformer (ViT)-based ensemble architecture for classifying breast ultrasound images.
  • To improve the accuracy and reliability of automated breast lesion classification.
  • To evaluate the performance of the proposed model on the Breast Ultrasound Images (BUSI) dataset.

Main Methods:

  • Utilized a Breast Ultrasound Images (BUSI) dataset comprising 133 normal, 437 benign, and 210 malignant images.
  • Applied Region of Interest (ROI) segmentation and image augmentation for focused analysis.
  • Employed three ViT models (ViT-Base, DeiT, ViT-Small) for feature extraction, followed by a multilayer perceptron (MLP) classifier.
  • Implemented 10-fold stratified cross-validation for robust model training and evaluation.

Main Results:

  • Achieved high precision and recall for benign (96.2% precision, 86.3% recall) and malignant (76.4% precision, 92.9% recall) classes.
  • Attained 100% success rate for the normal class.
  • Reported Area Under the Curve (AUC) values of 0.97 for benign, 0.96 for malignant, and 1.00 for normal classes.

Conclusions:

  • The ROI-based ViT + MLP + Ensemble architecture offers superior accuracy and explainability over traditional Convolutional Neural Network (CNN) methods.
  • Demonstrated stable performance, particularly for minority classes, highlighting its potential in clinical decision support.
  • Presents a reliable and flexible AI solution for breast ultrasound image classification.