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Enhanced Breast Cancer Diagnosis Using Multimodal Feature Fusion with Radiomics and Transfer Learning.

Nazmul Ahasan Maruf1, Abdullah Basuhail1, Muhammad Umair Ramzan1

  • 1Faculty of Computing and Information Technology, Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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Summary
This summary is machine-generated.

This study enhances breast cancer detection by integrating radiomics and deep learning (DL) features, achieving 97% accuracy with ResNet152. The approach improves diagnostic precision and robustness for early cancer identification.

Keywords:
breast cancerdeep learningfeature engineeringmedical imagingradiomics analysistransfer learning

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Computational Pathology
  • Biomedical Informatics

Background:

  • Breast cancer is a leading cause of cancer mortality globally, necessitating improved early detection methods.
  • Current early detection relies on medical imaging, with artificial intelligence (AI), radiomics, and deep learning (DL) showing promise.
  • Challenges such as limited data, overfitting, and poor generalization hinder AI model performance in breast cancer detection.

Purpose of the Study:

  • To enhance breast cancer detection accuracy and robustness by combining radiomics and deep learning (DL) features.
  • To overcome data limitations and model overfitting using advanced data augmentation techniques.
  • To develop a unified multimodal feature space for improved classification performance.

Main Methods:

  • Extracted radiomics features using PyRadiomics and deep learning features via transfer learning models from the CBIS-DDSM dataset.
  • Applied data augmentation to mitigate overfitting and improve model generalization.
  • Integrated radiomics and deep features, training and evaluating 13 pre-trained transfer learning models, including ResNet, DenseNet, InceptionV3, MobileNet, and VGG.

Main Results:

  • ResNet152 achieved the highest classification accuracy of 97%, demonstrating significant potential for enhanced diagnostic precision.
  • Other models like VGG19, ResNet101V2, and ResNet101 also showed high accuracy (96%), highlighting the effectiveness of the selected feature set.
  • The integrated multimodal feature space contributed to robust breast cancer detection.

Conclusions:

  • The combined radiomics and deep learning approach shows significant promise for accurate and robust breast cancer detection.
  • Future research should explore Vision Transformer (ViT) architectures and multimodal data integration (clinical, genomic) for further improvements.
  • This methodology has the potential to revolutionize breast cancer detection, making it more accurate, interpretable, and adaptable.