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Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples

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Abstract

Breast cancer is a prevalent and highly lethal cancer affecting women globally. While non-invasive techniques like ultrasound and mammogram are used for diagnosis, histological examination after biopsy is considered the gold standard. However, manual examination of tissues for abnormality is labor-intensive, expensive, and requires prior domain knowledge. Early detection, awareness, and access to specialized medical infrastructure in resource-constrained and remote areas are significant challenges but crucial for saving lives. In recent years, deep learning-based approaches have shown promising results in breast cancer detection, facilitated by advancements in GPU memory, computation power, and the availability of digital data. Motivated by these observations, we propose the Breast-NET deep convolutional neural network model for breast cancer detection and grading using histological images. Our model’s performance is evaluated on the BreakHis dataset, and we demonstrate its generalization ability on the Invasive Ductal Carcinoma (IDC) grading and IDC datasets. Extensive experimental and statistical performance analysis, along with an ablation study, validates the efficiency of our proposed model. Furthermore, we demonstrate the effectiveness of transfer learning with seven pre-trained convolutional neural networks for breast cancer detection and grading. Experimental results show that our framework outperforms state-of-the-art approaches in terms of accuracy, space, and computational complexity for the BreakHis, IDC grading, and IDC datasets.

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Data availability

The data that support the finding of this study are available from [22, 25, 41].

Notes

  1. Source code is available at https://github.com/mainak15/Breast-NET.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Durgapur, West Bengal, 713209, India

    Mousumi Saha & Deepanwita Das

  2. School of Innovation, Design and Engineering, Division of Intelligent Future Technologies, Malardalen University, Västerås, Sweden

    Mainak Chakraborty

  3. Department of Information Technology, Narula Institute of Technology, Kolkata, West Bengal, 700109, India

    Suchismita Maiti

  4. Department of Computer Science and Engineering, Narula Institute of Technology, Kolkata, West Bengal, 700109, India

    Mousumi Saha

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Correspondence to Mousumi Saha.

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Saha, M., Chakraborty, M., Maiti, S. et al. Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples. Neural Comput & Applic 36, 20067–20087 (2024). https://doi.org/10.1007/s00521-024-10298-9

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