Thèses en ligne de l'université 8 Mai 1945 Guelma

Classification des images mammographiques selon leurs densités

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dc.contributor.author Benmaamar, Oussama
dc.date.accessioned 2023-11-21T10:43:01Z
dc.date.available 2023-11-21T10:43:01Z
dc.date.issued 2023
dc.identifier.uri http://dspace.univ-guelma.dz/jspui/handle/123456789/14927
dc.description.abstract Breast cancer is a serious pathology in women, where early detection is of prime importance, as it considerably increases the chances of survival. Mammography is an essential technique in this process, but its interpretation is complex due to the variability of breast density. Currently, machine learning techniques, notably Deep Learning, are widely used for image classification. The main aim of this final-year project is to classify digital mammograms according to their density (B or C). The experiments in this project were carried out on the RSNA (The Radiological Society of North America) mammography database, consisting of 24350 mammograms. To reduce noise, some pre-processing techniques were applied, and classification was done based on transfer learning through pre- trained models such as VGG16, ResNet50, MobileNetV2 and EfficientNetB0. Among these models, EfficientNetB0 was elected as the best classification model because of these good results. This model achieved a recognition rate of 85.64%, a precision of 86%, a recall of 88% and an F1 score of 86%. en_US
dc.language.iso en en_US
dc.publisher university of guelma en_US
dc.subject transfer learning, mammography, classification, pre-processing. en_US
dc.title Classification des images mammographiques selon leurs densités en_US
dc.type Working Paper en_US


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