Mammography screening is the main examination for breast cancer early detection, and has shown important benefits in reducing advanced and fatal disease rates. In this paper a YoloV5 model for simulta- neous breast cancer localization and classification in mammograms is proposed. Two public dataset were used for training and test. The CBIS-DDSM dataset, composed of scanned film mammograms, was used as a source dataset to implement the Transfer Learning tech- nique on the target INbreast dataset, composed of Full-Field Digital mammograms. The Small YoloV5 model combined with a large data- augmentation strategy was the best developed solution. A improvement of 0.103 mAP was found when Transfer Learning technique was imple- mented on the INbreast dataset. The performance was encouraging, resulting in a mAP of 0.838 ± 0.042, Recall of 0.722 ± 0.096, and Precision of 0.917 ± 0.077, calculated using the 5-Fold CV. The recog- nition rate achieved with the Transfer Learning on Full-Field Digital mammograms, encouraging future analysis on a proprietary dataset.

Prinzi F., Insalaco M., Gaglio S., Vitabile S. (2023). Breast Cancer Localization and Classification in Mammograms Using YoloV5. In Smart Innovation, Systems and Technologies (pp. 73-82). Springer Science and Business Media Deutschland GmbH [10.1007/978-981-99-3592-5_7].

Breast Cancer Localization and Classification in Mammograms Using YoloV5

Prinzi F.
Primo
;
Gaglio S.;Vitabile S.
Ultimo
2023-08-02

Abstract

Mammography screening is the main examination for breast cancer early detection, and has shown important benefits in reducing advanced and fatal disease rates. In this paper a YoloV5 model for simulta- neous breast cancer localization and classification in mammograms is proposed. Two public dataset were used for training and test. The CBIS-DDSM dataset, composed of scanned film mammograms, was used as a source dataset to implement the Transfer Learning tech- nique on the target INbreast dataset, composed of Full-Field Digital mammograms. The Small YoloV5 model combined with a large data- augmentation strategy was the best developed solution. A improvement of 0.103 mAP was found when Transfer Learning technique was imple- mented on the INbreast dataset. The performance was encouraging, resulting in a mAP of 0.838 ± 0.042, Recall of 0.722 ± 0.096, and Precision of 0.917 ± 0.077, calculated using the 5-Fold CV. The recog- nition rate achieved with the Transfer Learning on Full-Field Digital mammograms, encouraging future analysis on a proprietary dataset.
2-ago-2023
Prinzi F., Insalaco M., Gaglio S., Vitabile S. (2023). Breast Cancer Localization and Classification in Mammograms Using YoloV5. In Smart Innovation, Systems and Technologies (pp. 73-82). Springer Science and Business Media Deutschland GmbH [10.1007/978-981-99-3592-5_7].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/620547
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