Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study

Sania Eskandari, Ali Eslamian, Nusrat Munia, Amjad Alqarni, Qiang Cheng
8/29/2024
eess.IVcs.CV

No official code found. Best implementation: saniaesk/Breast-Cancer-Classification (3 stars, last commit 2024-10-31).

Abstract

This study evaluates the effectiveness of deep learning models in classifying histopathological images for early and accurate detection of breast cancer. Eight advanced models, including ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet, were compared using a dataset of 277,524 image patches. The Vision Transformer (ViT) model, with its attention-based mechanisms, achieved the highest validation accuracy of 94%, outperforming conventional CNNs. The study demonstrates the potential of advanced machine learning methods to enhance precision and efficiency in breast cancer diagnosis in clinical settings.

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Code Implementations(1)

saniaesk/Breast-Cancer-ClassificationCommunity100%

Comparative Analysis of Transfer Learning Models for Breast Cancer Classification

30PythonOct 30, 20241 years ago

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Cite this paper

@article{eskandari2024evaluating,
  title  = {Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study},
  author = {Sania Eskandari and Ali Eslamian and Nusrat Munia and Amjad Alqarni and Qiang Cheng},
  year   = {2024},
  eprint = {2408.16859},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.16859v2}
}

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