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Breast cancer detection from histopathology images with deep inception and residual blocks

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Abstract

The rapid growth of the network in deep learning, a subset of artificial intelligence has motivated the researchers to develop the tools for medical imaging analysis. Here we have introduced computer aided diagnosis tools for binary class classification and detection for breast cancer on histopathological images. In this paper, we have proposed a hybrid deep neural network for image level cancer detection in cancerous and non-cancerous category of histopathology images. The hybrid deep neural network comprises of inception and residual block. The network incorporates the advance multilevel feature map for histopathological images and involve the advantages of inception and residual block. The model proposed combines the sturdiness of inception block and residual block and shows the stability in performance against the existing start-of the art algorithms. The proposed method is trained and validated on two publicly available dataset i.e., Breast Histopathology Images (BHI) and BreakHis. The image level classification has been performed at different magnification level in case of BreakHis dataset. The experimental outcome is evaluated on different performance measures and compared with the conventional Inception model and ResNet model as well as state-of-art breast cancer detection techniques. The proposed approach shows the training accuracy of 0.9642 for Breakhis and 0.8017 for BHI dataset. The model proposed outperforms the existing cancer detection algorithms as well as conventional deep neural networks with obtained accuracy of 0.8521 for BHI and 0.8080,0.8276,0.8655and 0.8580 for 40X,100X,200X and 400X respectively for BreakHis dataset.

Research Highlights

With an increase in the availability of huge pixel whole slide image (WSI) of tissues has given bloom to microscopic pathology application of deep learning and the possibility of loading the scanned images onto the machines, it has become easier for the researchers to develop an automated system for analyzing such images. The paper presents the deep learning-based approach for breast cancer for binary class classification. The proposed model has exploited the inception block of Inception V3 and residual block of Resnet. The proposed model is verified experimentally on both the dataset large (BHI) and small (BreakHis). The contribution of the paper can be summarized as-

  1. 1.

    Remarkable classification accuracy is achieved while working on the recent dataset. The two-step feature map extraction model is trained by combining different magnification levels. The dataset is classified into benign and malignant class.

  2. 2.

    The result is discussed on various performance measures for both the benchmark dataset.

  3. 3.

    The experimental outcomes are compared with the result of the conventional model of Inception and Resnet along with the latest work reported in the literature of deep learning for breast cancer.

  4. 4.

    It is concluded from the experimental result that the proposed model works competently with a large dataset as well as the small dataset. And magnification plays an important role.

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Authors and Affiliations

  1. Department of Electronics & Communication, JK Institute of Applied Physics & Technology, University of Allahabad, Prayagraj, India

    Shiksha Singh & Rajesh Kumar

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  1. Shiksha Singh
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  2. Rajesh Kumar
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Correspondence to Shiksha Singh.

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Singh, S., Kumar, R. Breast cancer detection from histopathology images with deep inception and residual blocks. Multimed Tools Appl 81, 5849–5865 (2022). https://doi.org/10.1007/s11042-021-11775-2

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