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Related Experiment Video

Updated: Jul 7, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Classifying Kidney Tumor via Adaptive SE-ResNeXt With Mutli-Novel Loss Function and Renal Mass Segmentation Using

Bipin Bihari Jayasingh1, H Niroshini Infantia2, Tejaswini Panse3

  • 1IT Department, CVR College of Engineering, Hyderabad, Telangana, India.

Journal of Biochemical and Molecular Toxicology
|April 27, 2026
PubMed
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An Ensemble CNN With Bayesian Learning Model for Multiclass Classification of Brain Disease Using Adaptive Refinement Network-Based Segmentation.

NMR in biomedicine·2025
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This study introduces a deep learning model for efficient kidney cancer detection. The model accurately segments and classifies tumors, aiding in early diagnosis and treatment planning.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Kidney tumors are a significant global health concern, with malignant forms requiring early detection for improved outcomes.
  • Traditional diagnostic methods for kidney tumors are labor-intensive and can be time-consuming.
  • Deep learning offers a promising approach to automate and enhance the accuracy of kidney cancer diagnosis.

Purpose of the Study:

  • To develop and evaluate a deep learning-based model for the efficient and accurate detection of kidney tumors.
  • To improve the speed and reduce the cost of kidney cancer diagnosis through automated algorithms.
  • To classify kidney tumors based on their characteristics to personalize and enhance therapeutic effectiveness.

Main Methods:

  • Utilized a Pyramidal Attention-based Recurrent Residual Unet++ (PA-R2Unet++) model for precise image segmentation of kidney tumors.
Keywords:
adaptive squeeze‐and‐excitation‐residual networks next with novel multi‐loss functionenhanced arbitrary value‐based Flamingo search algorithmkidney tumor segmentation and classificationpyramidal attention‐based recurrent residual Unet++

Related Experiment Videos

Last Updated: Jul 7, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

  • Employed an Adaptive Squeeze-And-Excitation-ResNeXt with Novel Multi-Loss Function (ASE-RX-NMLF) for tumor classification.
  • Optimized the classification model parameters using an Enhanced Arbitrary Value-based Flamingo Search Algorithm (EAV-FSA).

    • Main Results:

    • The developed PA-R2Unet++ model achieved accurate segmentation, enabling early identification of tumor development.
    • The ASE-RX-NMLF model, optimized by EAV-FSA, demonstrated high efficiency in classifying kidney tumors.
    • The proposed deep learning approach showed comparable or superior efficacy to existing models in kidney tumor detection.

    Conclusions:

    • The introduced deep learning model significantly speeds up the kidney cancer diagnosis process.
    • Accurate segmentation and classification are crucial for early detection and effective treatment of kidney tumors.
    • This automated approach holds potential for reducing healthcare costs and improving patient outcomes in urological oncology.