Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mouse Models of Cancer Study

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Transfer learning with Bayesian optimization for colorectal cancer histopathology classification.

BMC medical imaging·2026

Same author

Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis.

Journal of imaging·2025

Same author

VMDU-net: a dual encoder multi-scale fusion network for polyp segmentation with Vision Mamba and Cross-Shape Transformer integration.

Frontiers in artificial intelligence·2025

Same author

Synergistic Multi-Granularity Rough Attention UNet for Polyp Segmentation.

Journal of imaging·2025

Search research articles

Related Experiment Video

Updated: May 4, 2026

Modeling Colitis-Associated Cancer with Azoxymethane AOM and Dextran Sulfate Sodium DSS

Modeling Colitis-Associated Cancer with Azoxymethane AOM and Dextran Sulfate Sodium DSS

Published on: September 11, 2012

Fine-Tuning Models for Histopathological Classification of Colorectal Cancer.

Houda Saif ALGhafri¹, Chia S Lim²

¹Department of Information Technology, College of Computing and Information Sciences, University of Technology and Applied Sciences, Muscat 133, Oman.

Diagnostics (Basel, Switzerland)

|August 14, 2025

Summary

This study enhances colorectal cancer classification using fine-tuned deep learning models. Transfer learning strategies significantly improved accuracy and generalizability in histopathological image analysis.

Keywords:

colorectal cancer deep learning fine-tuning histopathological images transfer learning

More Related Videos

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Published on: March 29, 2021

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Related Experiment Videos

Last Updated: May 4, 2026

Modeling Colitis-Associated Cancer with Azoxymethane AOM and Dextran Sulfate Sodium DSS

Modeling Colitis-Associated Cancer with Azoxymethane AOM and Dextran Sulfate Sodium DSS

Published on: September 11, 2012

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Published on: March 29, 2021

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Area of Science:

Medical image analysis
Computational pathology
Deep learning in oncology

Background:

Accurate colorectal cancer (CRC) diagnosis relies on histopathological image analysis.
Improving the accuracy and generalizability of automated CRC classification is crucial for clinical practice.

Purpose of the Study:

To design and evaluate transfer learning strategies for CRC histopathological image classification.
To fine-tune multiple pre-trained convolutional neural network (CNN) architectures based on their unique characteristics.
To enhance the diagnostic potential of deep learning models in CRC detection.

Main Methods:

Proposed CRCHistoDense, CRCHistoIncep, and CRCHistoXcep models, algorithmically fine-tuned at varying depths.
Applied transfer learning using pre-trained models on specialized and multiple datasets (10,613 images).
Integrated Grad-CAM for visual explanations to validate model decision-making and improve transparency.

Main Results:

Achieved high average test accuracies: CRCHistoDense (99.34%), CRCHistoIncep (99.48%), and CRCHistoXcep (99.45%).
Demonstrated significant performance improvements across diverse datasets, including unseen data.
Statistical tests (t-tests, ANOVA, Kruskal-Wallis) confirmed the significance of the improvements (p < 0.05).

Conclusions:

Fine-tuning CNN architectures based on their characteristics significantly enhances CRC classification performance.
The proposed transfer learning strategies improve both accuracy and generalizability in histopathological image analysis.
These findings underscore the potential of deep learning models to advance CRC diagnostics.