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An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer

Khaled Alnowaiser1, Abeer Saber2, Esraa Hassan3

  • 1College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia.

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Summary

This study enhances medical image classification for early tumor detection using advanced deep learning models like VGG16. The optimized approach achieves high accuracy in identifying breast cancer (BC) from mammograms.

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Computer-aided diagnosis (CAD)

Background:

  • Medical image classification (IC) is vital for computer-aided diagnosis (CAD) systems.
  • Convolutional neural network (CNN) models show high performance and robustness in IC.
  • Early detection of tumors and disorders relies on accurate image analysis.

Purpose of the Study:

  • To develop an optimized deep learning model for accurate medical image classification.
  • To improve the early detection of breast cancer (BC) using enhanced IC techniques.
  • To evaluate the performance of VGG16 and DenseNet-121 models in BC classification.

Main Methods:

  • Utilized pre-trained DenseNet-121 and VGG-16 as feature extractors.
  • Incorporated bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction.
  • Employed Support Vector Machine (SVM) and Random Forest (RF) for classification, with hyperparameters optimized by a modified grey wolf optimization method.

Main Results:

  • The VGG16 model demonstrated powerful performance for BC classification on the MIAS dataset, achieving 99.86% accuracy.
  • High sensitivity (99.9%), specificity (99.7%), precision (97.1%), and AUC (1.0) were recorded on the MIAS dataset.
  • Comparable results were obtained on the INbreast dataset, with 99.4% overall accuracy.

Conclusions:

  • The proposed optimized deep learning model, particularly VGG16, is highly effective for breast cancer classification.
  • The integration of CNNs, BiLSTM, and optimization techniques significantly enhances diagnostic accuracy.
  • This approach shows great potential for improving early tumor detection in medical imaging.