Mam-Incept-Net: a novel inception model for precise interpretation of mammography images

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dc.contributor.authorTandirovic Gursel, Amira
dc.contributor.authorKaya, Yasin
dc.date.accessioned2026-02-27T07:32:58Z
dc.date.available2026-02-27T07:32:58Z
dc.date.issued2025
dc.description.abstractEarly diagnosis of breast cancer through periodic screening is a vital ally in the fight for survival. Mammography, recognized as one of the most widely used and cost-effective tools for detecting early signs of asymmetry, calcification, masses, and architectural distortion in breast tissue, plays a significant role in nearly all screening scenarios. However, the interpretation and scoring of mammograms is a complex multi-parameter process that frequently leads to false-positive and false-negative results. This article introduces a new deep-learning-based model that classifies mammograms according to the Breast Imaging Reporting and Data System (BI-RADS) assessment categories. The model is trained on a private dataset, intentionally excluding no BI-RADS categories. A novel deep neural network architecture is employed to more accurately classify breasts, including their boundaries, as regions of interest (ROIs). The ConvNeXt architecture serves as a feature extractor for lower-level features, which are then combined with the layers of a randomly initialized naive inception module to capture higher-level features. Diagnosis is achieved through three experimental tests, yielding accuracy rates ranging from 82.08% to 86.27%. These promising accuracy levels, in comparison to previous studies, can be attributed to a more comprehensive approach to addressing BI-RADS scoring challenges. In addition to pursuing further enhancements in accuracy, future research should consider integrating prior radiology reports to create a more realistic end-to-end computer-aided detection system.
dc.identifier.doi10.7717/peerj-cs.3149
dc.identifier.issn2376-5992
dc.identifier.pmid40989348
dc.identifier.urihttp://dx.doi.org/10.7717/peerj-cs.3149
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4399
dc.identifier.volume11
dc.identifier.wosWOS:001591534700001
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPeerJ Inc
dc.relation.ispartofPeerj Computer Science
dc.relation.publicationcategoryMakale - Uluslararas� Hakemli Dergi - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20260302
dc.subjectBreast cancer
dc.subjectMammogram
dc.subjectBI-RADS categories
dc.subjectDeep transfer learning
dc.subjectEarly diagnosis
dc.subjectDeep learning
dc.titleMam-Incept-Net: a novel inception model for precise interpretation of mammography images
dc.typeArticle

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