Breast cancer prediction using a pretrained CNN Model ResNet-50

Akinbowale Nathaniel Babatunde (), Bukola Fatimah Balogun (), Omosola Jacob Olabode (), Joseph Bamidele Awotunde () and Agbotiname Lucky Imoize ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 9, 1398-1415

Abstract: Early diagnosis and administration of suitable treatment can substantially enhance the probability of human survival from breast cancer. This study utilized a large dataset comprising thousands of labeled breast images representing various types of breast cancer to train and validate the ResNet-50 model. Important features were extracted from the images using the residual blocks of the network and then fine-tuned for optimal performance. The experiments demonstrated that the ResNet-50 model achieved a fair level of accuracy in differentiating various forms of breast cancer, such as benign, malignant, and others. The ResNet-50 performed reasonably well, identifying benign, malignant, and normal cases with 98% accuracy when using accuracy as the metric, 97% when using precision, and 100% when using recall. Consequently, the trained ResNet-50 model was combined with the Flask framework to generate a simple user interface. These results suggest that employing residual networks in detecting breast cancer can significantly aid in early diagnosis and treatment. This study has important implications for public health and medical practice, providing physicians with a valuable resource in their fight against breast tumors.

Keywords: Breast cancer; Deep convolutional neural network, Resnet-50; deep learning, Transfer learning. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/10138/3298 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:9:p:1398-1415:id:10138

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().