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New enhanced breast tumor detection approach in mammogram scans based on pre-processing and deep transfer learning techniques

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Abstract

Breast cancer has surpassed heart disease as the second most common cause of mortality among women. Amongst several imaging techniques, mammogram scans are considered the most accurate and effective technique for detecting and diagnosing breast cancer. In this study, we propose an effective mammography pre-processing strategy that has been proven through a classification stage to evaluate its effectiveness. Hence, propose an accurate breast tumor detection model as the first step toward cancer detection. Several filters were used in the first stage, which is the pre-processing. In the second phase, which is tumor detection, various deep learning techniques were employed, including transfer learning, data augmentation, and global pooling techniques. To achieve that, we proposed a CNN architecture for mammogram scan classification, where for the feature extraction phase we used transfer learning techniques, in which six pre-trained CNN models were used for feature extraction: InceptionResNetV2, EfficientNetB7, DenseNet201, MobileNetV2, ResNet152V2, and VGG16. Meanwhile, for the classification phase, instead of using traditional ML algorithms or fully connected layers, we used global pooling techniques. The obtained results were satisfying, putting InceptionResNetV2 and VGG16 trials ahead of the other feature extractors with 99.83% accuracy, followed by the MobileNetV2 trial with 99.42%. That was due to the well-chosen pre-processing filters. Meanwhile, the other models establish good results as well regarding the previous studies. As for the classification phase influence, using global average pooling was more suitable for the majority of the models, except for InceptionResNetV2 and MobileNetV2 feature extractors where global max pooling achieved better results. Additionally, we were able to determine the best parameters for each model, as well as the influencing criteria.

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Availability of data and materials

The dataset analysed during the current study are available in http://www.eng.usf.edu/cvprg/Mammography/Database.html

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Funding

This study was not funded. The authors have no relevant financial or non-financial interests to disclose.

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  1. LIM Laboratory, University of Laghouat Amar Telidji, Laghouat, Algeria

    Saida Sarra Boudouh & Mustapha Bouakkaz

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  1. Saida Sarra Boudouh
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  2. Mustapha Bouakkaz
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Correspondence to Saida Sarra Boudouh.

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Boudouh, S.S., Bouakkaz, M. New enhanced breast tumor detection approach in mammogram scans based on pre-processing and deep transfer learning techniques. Multimed Tools Appl 83, 27357–27378 (2024). https://doi.org/10.1007/s11042-023-16545-w

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  • DOI: https://doi.org/10.1007/s11042-023-16545-w

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