Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Harnessing hybrid stacking ensemble learning for accurate pulmonary embolism diagnosis using tabular clinical data.

Scientific reports·2026
Same author

Improved pulmonary embolism detection in CT pulmonary angiogram scans with hybrid vision transformers and deep learning techniques.

Scientific reports·2025
Same author

A hybrid gazelle optimization and reptile search algorithm for optimal clustering in wireless sensor networks.

Scientific reports·2025
Same author

A deep learning based hybrid recommendation model for internet users.

Scientific reports·2024
Same author

Enhancing colorectal cancer histology diagnosis using modified deep neural networks optimizer.

Scientific reports·2024
Same author

MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.

BMC bioinformatics·2024
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2025

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

6.8K

Fine tuning deep learning models for breast tumor classification.

Abeer Heikal1,2, Amir El-Ghamry3, Samir Elmougy3

  • 1Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt. abeerheikal@std.mans.edu.eg.

Scientific Reports
|May 10, 2024
PubMed
Summary
This summary is machine-generated.

This study improves breast tumor classification using a custom CNN and optimization techniques. MGTO optimization achieved 93.13% accuracy, outperforming other models for better breast cancer diagnosis.

More Related Videos

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Related Experiment Videos

Last Updated: Jun 26, 2025

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

6.8K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Accurate differentiation between benign and malignant breast tumors (BT) is crucial for effective treatment.
  • Histopathology images are vital for BT diagnosis, but manual analysis can be subjective and time-consuming.
  • Developing automated systems for BT classification can improve diagnostic accuracy and efficiency.

Purpose of the Study:

  • To propose and evaluate an approach for enhancing the classification of benign and malignant breast tumors using histopathology images.
  • To compare the performance of a custom Convolutional Neural Network (CNN) against pre-trained models for breast tumor differentiation.
  • To investigate the impact of metaheuristic optimization algorithms on improving CNN model performance for breast tumor classification.

Main Methods:

  • The study utilized the BreakHis dataset comprising histopathology images of breast tumors.
  • Preprocessing involved image resizing, data partitioning, and augmentation.
  • A custom CNN was developed for feature extraction and classification, with performance compared against pre-trained models (MobileNetV3, EfficientNetB0, Vgg16, ResNet50V2).
  • Hyperparameter tuning was performed using Grey Wolf Optimization (GWO) and Modified Gorilla Troops Optimization (MGTO) metaheuristic algorithms.

Main Results:

  • The custom CNN model achieved an initial accuracy of 84%, outperforming pre-trained models (74-82%).
  • After hyperparameter tuning with MGTO, the custom CNN model reached a significantly improved accuracy of 93.13% within 10 iterations.
  • MGTO optimization demonstrated superior performance in enhancing the classification accuracy compared to GWO and unoptimized models.

Conclusions:

  • The proposed approach, particularly the custom CNN optimized with MGTO, shows significant potential for accurate and efficient breast tumor classification.
  • Automated systems leveraging optimized deep learning models can aid pathologists in distinguishing benign from malignant breast tumors.
  • This research contributes to advancing AI-driven diagnostic tools in breast cancer research and clinical practice.