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Related Experiment Video

Updated: Apr 15, 2026

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

Published on: April 21, 2023

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Multiclass Classification of Renal Tumor Subtypes: Addressing Diagnostic Challenges Using a Texture-Informed Deep

Mohamed T Azam1, Hossam Magdy Balaha1, Ahmed Aboudessouki1

  • 1Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA.

Journal of Imaging Informatics in Medicine
|April 14, 2026
PubMed
Summary

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This summary is machine-generated.

A new AI framework using texture analysis and deep learning improves renal cell carcinoma (RCC) classification. This hybrid model enhances diagnostic accuracy, aiding precision oncology and patient care by distinguishing tumors from benign mimics.

Area of Science:

  • Oncology
  • Computer Science
  • Medical Imaging

Background:

  • Accurate histopathological classification of renal cell carcinoma (RCC) is crucial for effective patient management and precision oncology.
  • Diagnostic challenges arise from morphological complexity and subtle differences in RCC tumors, leading to inter-observer variability.

Purpose of the Study:

  • To develop a novel texture-informed hybrid deep learning framework for robust multiclass classification of renal cell neoplasms.
  • To address diagnostic challenges and reduce inter-observer variability in RCC histopathological classification.

Main Methods:

  • Integration of a Rotation-Invariant Multi-Threshold Local Binary Pattern (RIMT-LBP) descriptor with a cascaded CNN-Transformer architecture.
  • Utilized MobileNetV3Large for local feature extraction and Transformer encoders for global contextual modeling.
Keywords:
Deep learningHistopathologyLocal binary pattern (LBP)Multiclass classificationRenal cell carcinoma (RCC)Transformer encoder

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Last Updated: Apr 15, 2026

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  • Combined original images with RIMT-LBP features for enriched representation.

    • Main Results:

    • Achieved high whole slide image-level performance: 95.84% precision, 95.36% recall, 95.60% F1-score, and 98.10% accuracy.
    • The combined RGB+LBP approach outperformed RGB-only and LBP-only methods at the patch level (F1-scores: 89.18% vs. 84.44% and 80.31%).
    • Validated consistent performance on independent datasets (TCGA-RCC: 93.13% accuracy, DHMC: 96.12% accuracy).

    Conclusions:

    • The proposed texture-informed hybrid deep learning framework significantly improves RCC classification accuracy.
    • Integrating AI models with complementary analysis, including texture features, holds clinical potential for enhancing diagnostic accuracy.
    • The framework demonstrates robustness and consistency across different datasets, supporting its clinical applicability.