Transfer learning in breast mass detection and classification
Ryspayeva M. Bria A. Marrocco C. Tortorella F. Molinara M.
October 2024Springer Science and Business Media Deutschland GmbH
Journal of Ambient Intelligence and Humanized Computing
2024#15Issue 103587 - 3602 pp.
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.
Artificial intelligence , Breast cancer , Deep learning , Mammography , Mass classification , Mass detection , Transfer learning
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Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Viale dell’Università, Cassino, 03043, Italy
Department of Computer Engineering, Astana IT University, Mangilik El, block C.1, Astana, 010000, Kazakhstan
Department of Electrical, Information Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, 84084, Italy
Department of Electrical and Information Engineering
Department of Computer Engineering
Department of Electrical
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