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Breast Cancer classification via Deep Learning approaches

Author

Listed:
  • Marco Gagliardi
  • Tommaso Ruga
  • Ester Zumpano
  • Eugenio Vocaturo

Abstract

Breast cancer is the most common type of cancer in women worldwide. In 2023, there were 2.296.840 (23,8% of all women with cancer) new diagnoses. Early diagnosis is a key factor in reducing the mortality rate of breast cancer. One of the screening methods used to prevent breast cancer is breast ultrasound. In this paper, a new model is proposed that starts from a resnet101 and increases the classification capacity of a normal resnet101. Experimental studies show how deep learning models can successfully classify breast ultrasound images. The proposed model achieves 91% accuracy with convergence in less than 30 epochs. This study shows that deep learning models are effective in classifying ultrasound images and could be used by a radiologist to increase the accuracy of diagnoses.

Suggested Citation

  • Marco Gagliardi & Tommaso Ruga & Ester Zumpano & Eugenio Vocaturo, 2024. "Breast Cancer classification via Deep Learning approaches," SPAST Reports, SPAST Foundation, vol. 1(4).
    • Handle: RePEc:bps:jspath:v:1:y:2024:i:4:id:4992
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    File URL: https://spast.org/article/view/4992/419
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