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  1. Home
  3. Fine Tuning Deep Learning Models For Breast Tumor Classification.
  1. Home
  3. Fine Tuning Deep Learning Models For Breast Tumor Classification.

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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

View abstract on 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.

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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.