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Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning.

Basem S Abunasser1, Mohammed Rasheed J Al-Hiealy1, Ihab S Zaqout2

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A novel deep learning model (BCCNN) effectively detects and classifies eight breast cancer types from MRI images. High-resolution images, particularly 400X magnification, significantly improved diagnostic accuracy, enhancing patient survival rates.

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Early breast cancer (BC) detection significantly improves patient survival rates by 30-50%.
  • Deep learning models are increasingly vital for analyzing medical images like X-rays and MRIs.
  • Accurate classification of breast cancer subtypes is crucial for effective treatment planning.

Purpose of the Study:

  • To propose a deep learning model (BCCNN) for detecting and classifying breast cancer into eight distinct types.
  • To evaluate the performance of the proposed BCCNN model against five fine-tuned pre-trained deep learning models.
  • To assess the impact of image magnification on breast cancer detection and classification accuracy.

Main Methods:

  • Classified breast cancer MRI images into eight categories using a proposed BCCNN model and five fine-tuned models (Xception, InceptionV3, VGG16, MobileNet, ResNet50).
  • Utilized a breast cancer dataset from Kaggle, enhanced with Generative Adversarial Network (GAN) techniques for data augmentation.
  • Evaluated models across multiple image magnifications (40X, 100X, 200X, 400X) and complete datasets, conducting a total of 30 experiments.

Main Results:

  • The proposed BCCNN model achieved the highest F1-score accuracy at 98.28%.
  • Other models showed strong performance: ResNet50 (98.14%), VGG16 (97.67%), Xception (97.54%), InceptionV3 (95.33%), and MobileNet (93.98%).
  • Optimal classification accuracy was observed with 400X magnification MRI images due to their superior resolution.

Conclusions:

  • Dataset boosting, preprocessing, and balancing significantly enhanced the accuracy of both the proposed BCCNN and pre-trained models.
  • The 400X magnification MRI images yielded the best results, highlighting the importance of high-resolution imaging in breast cancer diagnostics.
  • The BCCNN model demonstrates a promising approach for accurate and efficient breast cancer detection and classification.