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CbcErDL: Classification of breast cancer from mammograms using enhance image reduction and deep learning framework

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Abstract

Breast cancer is a major health concern for women worldwide, and early detection is vital to improve treatment outcomes. While existing techniques in mammogram classification have demonstrated promising results, their limitations become apparent when applied to larger datasets. The decline in performance with increased dataset size highlights the need for further research and advancements in the field to enhance the scalability and generalizability of these techniques. In this study, we propose a framework to classify breast cancer from mammograms using techniques such as mammogram enhancement, discrete cosine transform (DCT) dimensionality reduction, and deep convolutional neural network (DCNN). The first step is to improve the mammogram display to improve the visibility of key features and reduce noise. For this, we use 2-stage Contrast Limited Adaptive Histogram Equalization (CLAHE). DCT is then used to enhance mammograms to reduce residual data. It can provide effective reduction while preserving important diagnostic information. In this way, we reduce the computational complexity and increase the results of subsequent classification algorithms. Finally, DCNN is used on size-reduced DCT coefficients to learn feature discrimination and classification of mammograms. DCNN architectures have been optimized with various techniques to improve their performance, including regularization and hyperparameter tuning. We perform experiments on the DDSM dataset, a large dataset containing approximately 55,000 mammogram images, and demonstrate the effectiveness of the proposed method. We assess the proposed model’s performance by computing the precision, recall, accuracy, F1-Score, and area under the receiver operating characteristic curve (AUC). We achieve Precision and Recall values of 0.929 and 0.963, respectively. The classification accuracy of the proposed models is 0.963. Moreover, the F1-Score and AUC values are 0.962 and 0.987, respectively. These results are better as compared to the standard techniques and the techniques from the literature. The proposed approach has the potential to assist radiologists in accurately diagnosing breast cancer, thereby facilitating early detection and timely intervention.

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Availability of data and materials

All the dataset used for the experiments are provided in [4, 5]. The required dataset is also cited in the manuscript.

Notes

  1. It is area under the ROC (receiver operating characteristic) curve

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Madhav Institute of Technology & Science, Race Course Road, Gwalior, MP, 474005, India

    Rohit Agrawal, Kuldeep Narayan Tripathi & Ranjeet Kumar Singh

  2. School of AI, Bennett University, Greater Noida, Uttar Pradesh, 201310, India

    Navneet Pratap Singh

  3. School of Computer Science and Engineering, Bennett University, TechZone 2, Greater Noida, UP, 201310, India

    Nitin Arvind Shelke

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  1. Rohit Agrawal
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  4. Kuldeep Narayan Tripathi
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Contributions

R.A. performed Investigation, writing-original draft, and performed experiments; N.P.S., N.A.S, R.K.S. and K.N.T performed a formal analysis; All authors reviewed the manuscript.

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Correspondence to Rohit Agrawal.

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Agrawal, R., Singh, N.P., Shelke, N.A. et al. CbcErDL: Classification of breast cancer from mammograms using enhance image reduction and deep learning framework. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19616-8

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