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Transfer learning in breast mass detection and classification

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Abstract

Covid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables the detection of suspicious lesions and classifying them in terms of malignancy. This paper aimed to investigate mass detection and classification in a large-scale OPTIMAM dataset with 6000 cases and extracted 3524 images with masses in the mammograms of the Hologic manufacturer. The methodology of the detection step is to train the RetinaNet architecture of ResNet50, ResNet101, and ResNet152 backbones with three types of initializations by ImageNet and COCO weights and from scratch. The dataset was pre-processed to generate two types of input with entire mammograms and patches, which are stated as the first and the second approaches. The results show that in the first approach, RetinaNet of ResNet50 backbone with ImageNet and COCO weights and ResNet152 with the same weights performed 0.91 True Positive Rate at 0.78 False Positive Per Image, respectively. In contrast, in the second approach, ResNet152 with ImageNet weights reached 0.88 TPR at 0.78 FPPI. In the classification step, the Transfer Learning approach was applied with fine-tuning by adding L2-regularization and class weights to balance class distribution in the datasets.

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Notes

  1. https://github.com/fizyr/keras-retinanet.

  2. https://www.tensorflow.org.

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Acknowledgements

The authors acknowledge the OPTIMAM project for providing the images used in this study, the staff at Surrey who developed OMI-DB, and Cancer Research Technology, which funded the OPTIMAM project through our charity, Cancer Research UK. This work was supported by MUR (Italian Ministry for University and Research) funding to AB, CM, and MM through the DIEI Department of Excellence 2018-2022 (law 232/2016) and to FT through the DIEM Department of Excellence 2023-2027 (law 232/2016). Marya Ryspayeva holds an EACEA Erasmus+ grant for the M.Sc. in Medical Imaging and Applications (MAIA). The EU partially supported this work in the NextGenerationEU plan through MUR Decree n. 1051 23.06.2022 “PNRR Missione 4 Componente 2 Investimento 1.5” - CUP H33C22000420001.

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Correspondence to Mario Molinara.

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Ryspayeva, M., Bria, A., Marrocco, C. et al. Transfer learning in breast mass detection and classification. J Ambient Intell Human Comput 15, 3587–3602 (2024). https://doi.org/10.1007/s12652-024-04835-6

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