Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

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dc.contributor.authorSubaar, Christiana
dc.contributor.authorAddai, Fosberg Tweneboah
dc.contributor.authorAddison, Eric Clement Kotei
dc.contributor.authorChristos, Olivia
dc.contributor.authorAdom, Joseph
dc.contributor.authorOwusu-Mensah, Martin
dc.contributor.authorAppiah-Agyei, Nelson
dc.contributor.authorAbbey, Shadrack
dc.contributor.orcid0000-0003-1158-5780
dc.date.accessioned2024-05-29T14:22:12Z
dc.date.available2024-05-29T14:22:12Z
dc.date.issued2024
dc.descriptionThis article is published in Biomedical Physics & Engineering Express, 10 (2024) 035029; https://doi.org/10.1088/2057-1976/ad3cdf
dc.description.abstractA lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases.Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society’s(NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.
dc.description.sponsorshipKNUST
dc.identifier.citationBiomedical Physics & Engineering Express, 10 (2024) 035029; https://doi.org/10.1088/2057-1976/ad3cdf
dc.identifier.urihttps://doi.org/10.1088/2057-1976/ad3cdf
dc.identifier.urihttps://ir.knust.edu.gh/handle/123456789/15731
dc.language.isoen
dc.publisherBiomedical Physics & Engineering Express
dc.titleInvestigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34
dc.typeArticle
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