Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms

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dc.contributor.authorTiryaki, Volkan Müjdat
dc.date.accessioned2024-12-24T19:16:14Z
dc.date.available2024-12-24T19:16:14Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractThe number of breast cancer diagnoses is the largest among all cancers among women in the world. Breast cancer treatment is possible if it is diagnosed in the early stages. Mammography is a common imaging technique to detect breast cancer abnormalities. Breast cancer symptom screening is being performed by radiologists. In the last decade, deep learning was successfully applied to big image classification databases such as the ImageNet. In this study, the breast cancer pathology classification performances of the recent deep learning models were investigated by transfer learning and fine tuning. A total of 3,360 mammogram patches were used from the Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) mammogram databases for deep learning model training, validating, and testing. Transfer learning and fine tuning were applied using Resnet50, Xception, NASNet, and EfficientNet-B7 network weights. The best classification performance was achieved by transfer learning from the Xception network. The computational costs of deep learning models were considered while selecting the best one. On the original CBIS-DDSM five-way test mammogram classification problem, the mean sensitivity, specificity, F1-score, and AUC were 0.7054, 0.9264, 0.7024, and 0.9317, respectively. The results show that the proposed models may be useful for the classification of breast cancer pathologies
dc.identifier.doi10.17798/bitlisfen.1190134
dc.identifier.endpage65
dc.identifier.issn2147-3129
dc.identifier.issn2147-3188
dc.identifier.issue1
dc.identifier.startpage57
dc.identifier.trdizinid1162211
dc.identifier.urihttps://doi.org/10.17798/bitlisfen.1190134
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1162211
dc.identifier.urihttps://hdl.handle.net/20.500.12604/4306
dc.identifier.volume12
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofBitlis Eren Üniversitesi Fen Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectBreast cancer
dc.subjectimage classification
dc.subjecttumor
dc.subjectnodule
dc.subjectcomputer-aided diagnosis
dc.titleDeep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms
dc.typeArticle

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