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A Multi-attention Triple Decoder Deep Convolution Network for Breast Cancer Segmentation Using Ultrasound Images

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Abstract

Breast cancer (BC) is a widely diagnosed deadly disease commonly present in middle-aged women around the globe. Ultrasound (U/S) imaging is widely used for the early prediction and segmentation of BC due to low radiation and cheapness. Manual BC segmentation from ultrasound imaging is a complex and laborious task due to inherited noise. Many deep learning-based breast cancer diagnostic methods are presented that can further be enhanced to improve the segmentation performance. This work proposed a U-shaped auto encoder-based multi-attention triple decoder convolution neural network for BC segmentation from U/S images. To capture multi-scale diverse spatial image features this work introduced a multi-scale convolution operation-based encoder network. To process the multi-scale learned diverse spatial features in the encoder path multi-scale triple decoder network is designed that was not found in earlier studies. To highlight the tumor region at different scales multi-attention mechanism is introduced in each decoder network. The multi-attention mechanism is designed to suppress the other region information and to highlight the tumor region features at different scales. The proposed deep network produced the segmentation dice of 90.45% on the UDIAT dataset and the segmentation dice of 89.13% on the BUSI dataset. The testing Jaccard index of 83.40% is recorded on the UDIAT dataset and a Jaccard index of 82.31% is recorded on the BUSI dataset. The result comparison with existing methods shows that our method achieved the highest results. The segmentation performance of the triple decoder-based BC segmentation model suggested that it can effectively be used to automate the manual breast cancer segmentation task from ultrasound images.

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Data Availability

This study utilized two publicly available datasets including the BUSI dataset by Dhabyani et al. [34] available at https://scholar.cu.edu.eg/?q=afahmy/pages/dataset and the UDIAT dataset B which can be accessed by mailing the principal author of the study Yap et al. [35].

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan

    Muhammad Junaid Umer & Muhammad Sharif

  2. Department of Computer Science, HITEC University Taxila, Taxila, Pakistan

    Mudassar Raza

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  1. Muhammad Junaid Umer
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Correspondence to Muhammad Sharif.

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Umer, M.J., Sharif, M. & Raza, M. A Multi-attention Triple Decoder Deep Convolution Network for Breast Cancer Segmentation Using Ultrasound Images. Cogn Comput 16, 581–594 (2024). https://doi.org/10.1007/s12559-023-10214-8

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