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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...

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Related Experiment Video

Updated: Jun 8, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Transfer Learning With Adam Gold Rush Optimization for Endometrial Disease Classification Using Histopathological

Sudhagar Dhandapani1, Ravikumar Subburam2, Pretty Diana Cyril Cyriloose3

  • 1Department of Information Technology, Jerusalem College of Engineering, Chennai, India.

Microscopy Research and Technique
|July 9, 2025
PubMed
Summary

A new Transfer Learning Convolution Neural Network with Adam Gold Rush Optimization (TL-CNN_AdGRO) accurately classifies endometrial cancer from histopathological images, improving early detection and patient survival rates.

Keywords:
Adam gold rush optimizationdeep learningdirectional connectivity‐based segmentationendometrial cancertransfer learning

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Endometrial cancer, a significant condition affecting female reproductive organs, necessitates early and accurate diagnosis for improved survival rates.
  • Current diagnostic methods for endometrial cancer can be enhanced through advanced computational techniques applied to histopathological images.

Purpose of the Study:

  • To propose a novel Transfer Learning based Convolution Neural Network with Adam Gold Rush Optimization (TL-CNN_AdGRO) for the classification of endometrial cancer.
  • To evaluate the performance of the proposed TL-CNN_AdGRO model in accurately identifying endometrial cancer from histopathological images.

Main Methods:

  • Histopathological images undergo preprocessing using an Adaptive Weighted Mean Filter (AWMF).
  • Endometrial cancer segmentation is performed using Directional Connectivity Network (DConn-Net).
  • Feature extraction includes Local Boundary Summation Pattern (LBSP) and Local Gaber Binary Pattern Histogram Sequence Features (LGBPHS), followed by classification using TL-CNN trained with the AdGRO algorithm.

Main Results:

  • The proposed TL-CNN_AdGRO model achieved a high accuracy of 91.876%.
  • Superior performance metrics include a True Positive Rate (TPR) of 93.987% and a True Negative Rate (TNR) of 89.876% (K-sample 8).
  • The model demonstrated robustness and effectiveness compared to existing methods.

Conclusions:

  • The TL-CNN_AdGRO model shows significant promise for the early detection of endometrial cancer.
  • This approach offers a robust and effective method for histopathological image analysis in oncology.
  • The findings support the clinical utility of advanced AI models for improving cancer diagnostics.