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An Integrated Multimodal-Based CAD System for Breast Cancer Diagnosis.

Amal Sunba1,2, Maha AlShammari1,3, Afnan Almuhanna4

  • 1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

Cancers
|November 27, 2024
PubMed
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This study enhances breast cancer diagnosis by combining patient data with mammogram images from both breasts. Integrating statistical and image features significantly improves tumor classification accuracy in computer-aided diagnosis systems.

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Breast cancer remains a leading cause of mortality in women, necessitating advancements in diagnosis and treatment.
  • Computer-aided diagnosis (CAD) systems aim to assist physicians in improving breast tumor detection accuracy.
  • Existing CAD systems often analyze limited data, highlighting a need for more comprehensive approaches.

Purpose of the Study:

  • To investigate the effectiveness of combining patient information with mammogram features for breast tumor classification.
  • To compare two novel data integration approaches for enhancing CAD system performance.
  • To evaluate the impact of using bilateral mammogram views versus unilateral views.

Main Methods:

  • Developed two combination approaches: soft voting using statistical models and CNN, and feature concatenation in a deep learning model.
Keywords:
breast cancerbreast cancer diagnosisbreast tumorclassificationcomputer-aided diagnosis systemsconventional neural networkdeep CNNdisease diagnosesmachine learningmammogrammulti-layer perceptronsoft voting

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  • Utilized patient data (medical history, breast density, age) and mammogram features from both breasts (CC and MLO views).
  • Trained and evaluated models on a novel dataset encompassing normal, benign, and malignant breast tumor classes.

    • Main Results:

    • The soft voting approach achieved 90% accuracy in breast tumor classification.
    • Concatenating statistical and image-based features in a deep learning model yielded 93% accuracy.
    • Utilizing mammograms from both breasts and integrating patient information significantly outperformed single-side analysis and traditional methods.

    Conclusions:

    • Combining patient information with bilateral mammogram features substantially enhances breast tumor classification accuracy.
    • The deep learning model integrating statistical and image features shows significant promise for improving CAD systems.
    • This integrated approach offers a more robust and accurate method for breast cancer diagnosis.