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Deep quaternion convolutional neural networks for breast Cancer classification

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Abstract

Breast Cancer nowadays has been a major cause of death in women worldwide and this has also been confirmed by the World Health Organization. The severity of this disease can be minimized to a large extent if it is diagnosed properly at an early stage. Two important types of tumors found in the case of breast cancer are malignant and benign. Moreover, It has been observed that, unlike benign tumors, malignant tumors are more dangerous because of their invasive nature. Therefore, the proper treatment of a patient having cancer can be processed in a better way, if the type of tumor can be identified as early as possible. Deep neural networks have delivered a remarkable performance for detecting malignant tumors in histopathological images of breast tissues. However, the existing works today, are focused much on real-valued numbers. When data is multi-channel such as images and audio, conventional real-valued CNN on flattening and concatenating loses spatial relation within a channel. To address the above-said issues, we have exploited a quaternion residual network for detecting breast cancer in a dataset of histopathological images, which are publically available in the dataset of Kaggle. In this work, we first transform breast histopathological images into quaternion domains. Second, the Residual CNN was customized to work in the quaternion domain so that it extracts the better representative features for multidimensional input objects. Extensive experimental results demonstrate that our model architecture although takes slightly more time to train but it offers an increased classification accuracy of 97.20% which is more than the performance of a residual network compatible with real numbers. Also, the proposed model outperforms when compared against the baseline neural network models.

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Data availability

The dataset employed in the proposed work is mentioned in 3.1 [25].

Abbreviations

CAD:

Computer-aided detection

CNN:

Convolutional neural network

QCNN:

Quaternion Convolutional neural network

IDC:

Invasive Ductal Carcinoma

BHI:

Breast Histopathology Images

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Authors and Affiliations

  1. Information Technology Department, JSS Academy of Technical Education, Noida, India

    Sukhendra Singh

  2. Computer Science Department, Harcourt Butler Technological University Kanpur, Kanpur, India

    B. K. Tripathi

  3. Computer Science Department, JSS Academy of Technical Education, Noida, India

    Sur Singh Rawat

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  1. Sukhendra Singh
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  2. B. K. Tripathi
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  3. Sur Singh Rawat
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Research Highlights

The classification was performed on patches of 50 × 50 extracted from whole slide images of Breast cancer specimens. We proposed deep quaternion CNN architecture. Our models take advantage of computation utilizing Quaternion algebra and hypercomplex spaces, enabling not only expressive inter-channel interactions but also reduced parameter size due to fewer degrees of freedom in the Hamilton product. The suggested model processes the color image components as a unified unit without losing the spectral relationship between them, hence facilitating the processing of all color image components. The proposed model yielded an accuracy of 97.20%.

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Singh, S., Tripathi, B.K. & Rawat, S.S. Deep quaternion convolutional neural networks for breast Cancer classification. Multimed Tools Appl 82, 31285–31308 (2023). https://doi.org/10.1007/s11042-023-14688-4

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