Breast Cancer classification via Deep Learning approaches

Marco Gagliardi (), Tommaso Ruga (), Ester Zumpano () and Eugenio Vocaturo ()

SPAST Reports, 2024, vol. 1, issue 4

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.

Keywords: Deep learning; Breast cancer; Classification; Convolutional Neural Network (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bps:jspath:v:1:y:2024:i:4:id:4992

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