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M2S2-FNet: Multi-scale, Multi-stream feature network with Attention mechanism for classification of breast histopathological image

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Abstract

Breast cancer (BC) is the most commonly diagnosed cancer type, especially in women. Identifying the subtype of malignant (cancer) lesions can help to give proper treatment to cancer patients. The subtype of benign lesions can help to estimate the risk of developing cancer in the future. The computer-aided diagnosis (CAD) is an automated and more precise method for histopathological image classification. With the recent development in computer vision and deep learning, advanced convolution neural networks have achieved great success in image classification and widely used in medical image processing. This paper proposes a deep based multi-scale multi-stream feature network (M2S2-FNet) with attention mechanism for classification H&E stained breast histopathological microscopy image either as benign or malignant, and then categorizing malignant and benign cases into four different subtypes each. The proposed network processes the patch of breast cancer image through each multi-scale multi-stream to extract the robust; and refined features from the attention module. The proposed M2S2-FNet follows the knowledge of sharing strategy by sharing learned features at each stream across the network and the attention mechanism. M2S2-FNet achieved an accuracy for different magnification factors (× 40, × 100, × 200, and × 400) but the superior accuracy i.e. 98.07% for multi-class and 99.60% for binary at × 200. Accuracy, performance measure parameters like Recall, Precision and Sensitivity etc. and weight of M2S2-FNet model were better than existing models like VGG16, Xception, ResNet152 and BreastNet.

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, SVERIs College of Engineering, Pandharpur, University of PAH, Solapur, India

    Suvarna D. Pujari, Meenakshi M. Pawer & Swati P. Pawar

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  1. Suvarna D. Pujari
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  2. Meenakshi M. Pawer
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Pujari, S.D., Pawer, M.M. & Pawar, S.P. M2S2-FNet: Multi-scale, Multi-stream feature network with Attention mechanism for classification of breast histopathological image. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17717-4

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